TMLR 2024

955 papers

'Explaining RL Decisions with Trajectories’: A Reproducibility Study Karim Ahmed Abdel Sadek, Matteo Nulli, Joan Velja, Jort Vincenti

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“Studying How to Efficiently and Effectively Guide Models with Explanations” - A Reproducibility Study Adrian Sauter, Milan Miletić, Ryan Ott, Rohith Saai Pemmasani Prabakaran

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[Re] Classwise-Shapley Values for Data Valuation Markus Semmler, Miguel de Benito Delgado

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[Re] CUDA: Curriculum of Data Augmentation for Long‐tailed Recognition Barath Chandran.C

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[Re] Explaining Temporal Graph Models Through an Explorer-Navigator Framework Miklós Hamar, Matey Krastev, Kristiyan Danielov Hristov, David Beglou

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[Re] GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks Ana Vasilcoiu, T.H.F. Stessen, Thies Kersten, Batu Helvacioglu

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[Re] on the Reproducibility of Post-Hoc Concept Bottleneck Models Nesta Midavaine, Gregory Hok Tjoan Go, Diego Canez, Ioana Simion, Satchit Chatterji

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[Re] Reproducibility Study of “Explaining Temporal Graph Models Through an Explorer-Navigator Framework" Helia Ghasemi, Christina Isaicu, Jesse Wonnink, Andreas Berentzen

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***FastDoc***: Domain-Specific Fast Continual Pre-Training Technique Using Document-Level Metadata and Taxonomy Abhilash Nandy, Manav Nitin Kapadnis, Sohan Patnaik, Yash Parag Butala, Pawan Goyal, Niloy Ganguly

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$\clubsuit$ CLOVER $\clubsuit$: Probabilistic Forecasting with Coherent Learning Objective Reparameterization Kin G. Olivares, Geoffrey Négiar, Ruijun Ma, Oinam Nganba Meetei, Mengfei Cao, Michael W. Mahoney

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$\sigma$-PCA: A Building Block for Neural Learning of Identifiable Linear Transformations Fahdi Kanavati, Lucy Katsnith, Masayuki Tsuneki

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3D Molecular Generation via Virtual Dynamics Shuqi Lu, Lin Yao, Xi Chen, Hang Zheng, Di He, Guolin Ke

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A Bag of Tricks for Few-Shot Class-Incremental Learning Shuvendu Roy, Chunjong Park, Aldi Fahrezi, Ali Etemad

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A Density Estimation Perspective on Learning from Pairwise Human Preferences Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann Dauphin

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A Distance-Based Anomaly Detection Framework for Deep Reinforcement Learning Hongming Zhang, Ke Sun, Bo Xu, Linglong Kong, Martin Müller

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A Dual-Perspective Approach to Evaluating Feature Attribution Methods Yawei Li, Yang Zhang, Kenji Kawaguchi, Ashkan Khakzar, Bernd Bischl, Mina Rezaei

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A Fisher-Rao Gradient Flow for Entropic Mean-Field Min-Max Games Razvan-Andrei Lascu, Mateusz B. Majka, Lukasz Szpruch

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A Fully Decentralized Surrogate for Multi-Agent Policy Optimization Kefan Su, Zongqing Lu

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A General Framework for Formulating Structured Variable Selection Guanbo Wang, Mireille Schnitzer, Tom Chen, Rui Wang, Robert W Platt

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A General-Purpose Multi-Modal OOD Detection Framework Viet Quoc Duong, Qiong Wu, Zhengyi Zhou, Eric Zavesky, WenLing Hsu, Han Zhao, Huajie Shao

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A Globally Convergent Algorithm for Neural Network Parameter Optimization Based on Difference-of-Convex Functions Daniel Tschernutter, Mathias Kraus, Stefan Feuerriegel

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A Greedy Hierarchical Approach to Whole-Network Filter-Pruning in CNNs Kiran Purohit, Anurag Reddy Parvathgari, Sourangshu Bhattacharya

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A Joint Study of Phrase Grounding and Task Performance in Vision and Language Models Noriyuki Kojima, Hadar Averbuch-Elor, Yoav Artzi

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A Large-Scale 3D Face Mesh Video Dataset via Neural Re-Parameterized Optimization Kim Youwang, Lee Hyun, Kim Sung-Bin, Suekyeong Nam, Janghoon Ju, Tae-Hyun Oh

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A Lennard-Jones Layer for Distribution Normalization Mulun Na, Jonathan Klein, Biao Zhang, Wojtek Palubicki, Soren Pirk, Dominik Michels

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A Multilinear Least-Squares Formulation for Sparse Tensor Canonical Correlation Analysis Jun Yu, Zhaoming Kong, Kun Chen, Xin Zhang, Yong Chen, Lifang He

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A Note on Regularised NTK Dynamics with an Application to PAC-Bayesian Training Eugenio Clerico, Benjamin Guedj

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A Note on the Convergence of Denoising Diffusion Probabilistic Models Sokhna Diarra Mbacke, Omar Rivasplata

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A Persistent Homology-Based Algorithm for Unsupervised Anomaly Detection in Time Series Alexandre Bois, Brian Tervil, Laurent Oudre

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A Practical Guide to Sample-Based Statistical Distances for Evaluating Generative Models in Science Sebastian Bischoff, Alana Darcher, Michael Deistler, Richard Gao, Franziska Gerken, Manuel Gloeckler, Lisa Haxel, Jaivardhan Kapoor, Janne K Lappalainen, Jakob H. Macke, Guy Moss, Matthijs Pals, Felix C Pei, Rachel Rapp, A Erdem Sağtekin, Cornelius Schröder, Auguste Schulz, Zinovia Stefanidi, Shoji Toyota, Linda Ulmer, Julius Vetter

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A Probabilistic Model Behind Self- Supervised Learning Alice Bizeul, Bernhard Schölkopf, Carl Allen

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A Pseudo-Metric Between Probability Distributions Based on Depth-Trimmed Regions Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo, Stephan Clémençon, Florence d'Alché-Buc

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A Replica Analysis of Under-Bagging Takashi Takahashi

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A Review of the Applications of Deep Learning-Based Emergent Communication Brendon Boldt, David R Mortensen

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A Self-Representation Learning Method for Unsupervised Feature Selection Using Feature Space Basis Prayag Tiwari, Farid Saberi Movahed, Saeed Karami, Farshad Saberi-Movahed, Jens Lehmann, Sahar Vahdati

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A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks Forough Fazeli-Asl, Michael Minyi Zhang, Lizhen Lin

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A Short Survey on Importance Weighting for Machine Learning Masanari Kimura, Hideitsu Hino

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A Simple Video Segmenter by Tracking Objects Along Axial Trajectories Ju He, Qihang Yu, Inkyu Shin, Xueqing Deng, Alan Yuille, Xiaohui Shen, Liang-Chieh Chen

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A Single Transformer for Scalable Vision-Language Modeling Yangyi Chen, Xingyao Wang, Hao Peng, Heng Ji

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A Study of the Effects of Transfer Learning on Adversarial Robustness Pratik Vaishnavi, Kevin Eykholt, Amir Rahmati

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A Survey of Temporal Credit Assignment in Deep Reinforcement Learning Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, Laura Toni

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A Survey on Compositional Learning of AI Models: Theoretical and Experimental Practices Sania Sinha, Tanawan Premsri, Parisa Kordjamshidi

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A Survey on Data Selection for Language Models Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, Colin Raffel, Shiyu Chang, Tatsunori Hashimoto, William Yang Wang

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A Survey on Fairness Without Demographics Patrik Joslin Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji

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A Survey on Graph Construction for Geometric Deep Learning in Medicine: Methods and Recommendations Tamara T. Müller, Sophie Starck, Alina Dima, Stephan Wunderlich, Kyriaki-Margarita Bintsi, Kamilia Zaripova, Rickmer Braren, Daniel Rueckert, Anees Kazi, Georgios Kaissis

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A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law Zhiyu Chen, Jing Ma, Xinlu Zhang, Nan Hao, An Yan, Armineh Nourbakhsh, Xianjun Yang, Julian McAuley, Linda Ruth Petzold, William Yang Wang

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A Survey on Out-of-Distribution Detection in NLP Hao Lang, Yinhe Zheng, Yixuan Li, Jian Sun, Fei Huang, Yongbin Li

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A Survey on Transferability of Adversarial Examples Across Deep Neural Networks Jindong Gu, Xiaojun Jia, Pau de Jorge, Wenqian Yu, Xinwei Liu, Avery Ma, Yuan Xun, Anjun Hu, Ashkan Khakzar, Zhijiang Li, Xiaochun Cao, Philip Torr

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A Theoretical Framework for Zeroth-Order Budget Convex Optimization François Bachoc, Tommaso Cesari, Roberto Colomboni, Andrea Paudice

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A Theoretical Study of the Effects of Adversarial Attacks on Sparse Regression Deepak Maurya, Jean Honorio

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A True-to-the-Model Axiomatic Benchmark for Graph-Based Explainers Corrado Monti, Paolo Bajardi, Francesco Bonchi, André Panisson, Alan Perotti

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A Unified Hallucination Mitigation Framework for Large Vision-Language Models Yue Chang, Liqiang Jing, Xiaopeng Zhang, Yue Zhang

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A Unified View of Differentially Private Deep Generative Modeling Dingfan Chen, Raouf Kerkouche, Mario Fritz

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A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning Ran Wei, Nathan Lambert, Anthony D McDonald, Alfredo Garcia, Roberto Calandra

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A VAE-Based Framework for Learning Multi-Level Neural Granger-Causal Connectivity Jiahe Lin, Huitian Lei, George Michailidis

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Accelerated Deep Active Learning with Graph-Based Sub- Sampling Dan Kushnir, Shiyun Xu

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Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-Creation Zhiwei Zhang, Yuliang Liu

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Accurate Neural Network Pruning Requires Rethinking Sparse Optimization Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh

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Achieving the Asymptotically Minimax Optimal Sample Complexity of Offline Reinforcement Learning: A DRO-Based Approach Yue Wang, Jinjun Xiong, Shaofeng Zou

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Active Learning for Level Set Estimation Using Randomized Straddle Algorithms Yu Inatsu, Shion Takeno, Kentaro Kutsukake, Ichiro Takeuchi

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Active Sequential Two-Sample Testing Weizhi Li, Prad Kadambi, Pouria Saidi, Karthikeyan Natesan Ramamurthy, Gautam Dasarathy, Visar Berisha

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AdaFed: Fair Federated Learning via Adaptive Common Descent Direction Shayan Mohajer Hamidi, En-Hui Yang

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AdaFlood: Adaptive Flood Regularization Wonho Bae, Yi Ren, Mohamed Osama Ahmed, Frederick Tung, Danica J. Sutherland, Gabriel L. Oliveira

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Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection Nikolas Adaloglou, Felix Michels, Tim Kaiser, Markus Kollmann

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Adaptive Conformal Regression with Split-Jackknife+ Scores Nicolas Deutschmann, Mattia Rigotti, Maria Rodriguez Martinez

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Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning M Yashwanth, Gaurav Kumar Nayak, Arya Singh, Yogesh Simmhan, Anirban Chakraborty

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Adaptive Training Distributions with Scalable Online Bilevel Optimization David Grangier, Pierre Ablin, Awni Hannun

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Adaptively Robust and Sparse $k$-Means Clustering Hao Li, Shonosuke Sugasawa, Shota Katayama

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AdaStop: Adaptive Statistical Testing for Sound Comparisons of Deep RL Agents Timothée Mathieu, Matheus Medeiros Centa, Riccardo Della Vecchia, Hector Kohler, Alena Shilova, Odalric-Ambrym Maillard, Philippe Preux

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AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis Han Yu, Peikun Guo, Akane Sano

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Addressing Attribute Bias with Adversarial Support-Matching Thomas Kehrenberg, Myles Bartlett, Viktoriia Sharmanska, Novi Quadrianto

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Adversarial Attacks on Online Learning to Rank with Stochastic Click Models Zichen Wang, Rishab Balasubramanian, Hui Yuan, Chenyu Song, Mengdi Wang, Huazheng Wang

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Adversarial Imitation Learning from Visual Observations Using Latent Information Vittorio Giammarino, James Queeney, Ioannis Paschalidis

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Adversarially Robust Spiking Neural Networks Through Conversion Ozan Ozdenizci, Robert Legenstein

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Affordable Generative Agents Yangbin Yu, Qin Zhang, Junyou Li, Qiang Fu, Deheng Ye

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AGALE: A Graph-Aware Continual Learning Evaluation Framework Tianqi Zhao, Alan Hanjalic, Megha Khosla

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AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning Subhojeet Pramanik, Esraa Elelimy, Marlos C. Machado, Adam White

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AGG: Amortized Generative 3D Gaussians for Single Image to 3D Dejia Xu, Ye Yuan, Morteza Mardani, Sifei Liu, Jiaming Song, Zhangyang Wang, Arash Vahdat

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AmbientFlow: Invertible Generative Models from Incomplete, Noisy Measurements Varun A. Kelkar, Rucha Deshpande, Arindam Banerjee, Mark Anastasio

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Amortized Bayesian Decision Making for Simulation-Based Models Mila Gorecki, Jakob H. Macke, Michael Deistler

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An Attentive Approach for Building Partial Reasoning Agents from Pixels Safa Alver, Doina Precup

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An Improved Federated Clustering Algorithm with Model-Based Clustering Harsh Vardhan, Avishek Ghosh, Arya Mazumdar

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An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Alex Beeson, David Ireland, Giovanni Montana

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An Optimal Control Perspective on Diffusion-Based Generative Modeling Julius Berner, Lorenz Richter, Karen Ullrich

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Analysis of Classifier-Free Guidance Weight Schedulers Xi Wang, Nicolas Dufour, Nefeli Andreou, Marie-Paule Cani, Victoria Fernandez Abrevaya, David Picard, Vicky Kalogeiton

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Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning Ilya Kaufman, Omri Azencot

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Analyzing the Impact of Learnable SoftMax Temperature in Contrastive Visual-Textual Alignment Systems: Benefits, Drawbacks, and Alternative Approaches Zhun Sun, Chao Li

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Anomaly Detection with Semi-Supervised Classification Based on Risk Estimators Le Thi Khanh Hien, Sukanya Patra, Souhaib Ben Taieb

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Anticipatory Music Transformer John Thickstun, David Leo Wright Hall, Chris Donahue, Percy Liang

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AnyV2V: A Tuning-Free Framework for Any Video-to-Video Editing Tasks Max Ku, Cong Wei, Weiming Ren, Huan Yang, Wenhu Chen

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APBench: A Unified Availability Poisoning Attack and Defenses Benchmark Tianrui Qin, Xitong Gao, Juanjuan Zhao, Kejiang Ye, Cheng-zhong Xu

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Application of Bagged Copula-GP: Confirming Neural Dependency on Pupil Dilation Maximilian Walden

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Appropriate Balance of Diversification and Intensification Improves Performance and Efficiency of Adversarial Attacks Keiichiro Yamamura, Issa Oe, Nozomi Hata, Hiroki Ishikura, Katsuki Fujisawa

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Approximations to the Fisher Information Metric of Deep Generative Models for Out-of-Distribution Detection Sam Dauncey, Christopher C. Holmes, Christopher Williams, Fabian Falck

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Archetypal Analysis++: Rethinking the Initialization Strategy Sebastian Mair, Jens Sjölund

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Are Population Graphs Really as Powerful as Believed? Tamara T. Müller, Sophie Starck, Kyriaki-Margarita Bintsi, Alexander Ziller, Rickmer Braren, Georgios Kaissis, Daniel Rueckert

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Are You Using Test Log-Likelihood Correctly? Sameer Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick

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As Large as It Gets – Studying Infinitely Large Convolutions via Neural Implicit Frequency Filters Julia Grabinski, Janis Keuper, Margret Keuper

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ASPEST: Bridging the Gap Between Active Learning and Selective Prediction Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan O Arik, Somesh Jha, Tomas Pfister

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Assessing Biomedical Knowledge Robustness in Large Language Models by Query-Efficient Sampling Attacks Rui Patrick Xian, Alex Jihun Lee, Satvik Lolla, Vincent Wang, Russell Ro, Qiming Cui, Reza Abbasi-Asl

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Assessing Robustness via Score-Based Adversarial Image Generation Marcel Kollovieh, Lukas Gosch, Marten Lienen, Yan Scholten, Leo Schwinn, Stephan Günnemann

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Asynchronous Training Schemes in Distributed Learning with Time Delay Haoxiang Wang, Zhanhong Jiang, Chao Liu, Soumik Sarkar, Dongxiang Jiang, Young M Lee

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Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis, Julia Kempe

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Attending to Graph Transformers Luis Müller, Mikhail Galkin, Christopher Morris, Ladislav Rampášek

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Attention Normalization Impacts Cardinality Generalization in Slot Attention Markus Krimmel, Jan Achterhold, Joerg Stueckler

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Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data Samuel C Hoffman, Payel Das, Karthikeyan Shanmugam, Kahini Wadhawan, Prasanna Sattigeri

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Audio-Visual Dataset Distillation Saksham Singh Kushwaha, Siva Sai Nagender Vasireddy, Kai Wang, Yapeng Tian

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Augment Then Smooth: Reconciling Differential Privacy with Certified Robustness Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot

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Augmenting Ad-Hoc IR Dataset for Interactive Conversational Search Pierre Erbacher, Jian-Yun Nie, Philippe Preux, Laure Soulier

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AutoCLIP: Auto-Tuning Zero-Shot Classifiers for Vision-Language Models Jan Hendrik Metzen, Piyapat Saranrittichai, Chaithanya Kumar Mummadi

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AutoDocSegmenter: A Geometric Approach Towards Self-Supervised Document Segmentation Ankita Chatterjee, Anjali Raj, Soumyadeep Dey, Pratik Jawanpuria, Jayanta Mukhopadhyay, Partha Pratim Das

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Autoencoding Hyperbolic Representation for Adversarial Generation Eric Qu, Dongmian Zou

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Automated Design of Metaheuristic Algorithms: A Survey Qi Zhao, Qiqi Duan, Bai Yan, Shi Cheng, Yuhui Shi

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Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach Huy V. Vo, Vasil Khalidov, Timothée Darcet, Théo Moutakanni, Nikita Smetanin, Marc Szafraniec, Hugo Touvron, Camille Couprie, Maxime Oquab, Armand Joulin, Herve Jegou, Patrick Labatut, Piotr Bojanowski

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AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, Marius Lindauer

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Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithms Timothée Mathieu, Debabrota Basu, Odalric-Ambrym Maillard

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Bandits with Mean Bounds Nihal Sharma, Soumya Basu, Karthikeyan Shanmugam, Sanjay Shakkottai

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BaSIS-Net: From Point Estimate to Predictive Distribution in Neural Networks - A Bayesian Sequential Importance Sampling Framework Giuseppina Carannante, Nidhal Bouaynaya, Ghulam Rasool, Lyudmila Mihaylova

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Bayesian Computation Meets Topology Julius von Rohrscheidt, Bastian Rieck, Sebastian M Schmon

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Bayesian Optimization with Derivatives Acceleration Guillaume Perrin, Rodolphe Le Riche

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Bayesian Quantification with Black-Box Estimators Albert Ziegler, Paweł Czyż

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BBCaL: Black-Box Backdoor Detection Under the Causality Lens Mengxuan Hu, Zihan Guan, Junfeng Guo, Zhongliang Zhou, Jielu Zhang, Sheng Li

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Best-of-Both-Worlds Linear Contextual Bandits Masahiro Kato, Shinji Ito

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Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models Avi Singh, John D Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T Parisi, Abhishek Kumar, Alexander A Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura A Culp, Lechao Xiao, Maxwell Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

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Beyond Labeling Oracles - What Does It Mean to Steal ML Models? Avital Shafran, Ilia Shumailov, Murat A Erdogdu, Nicolas Papernot

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Beyond Loss Functions: Exploring Data-Centric Approaches with Diffusion Model for Domain Generalization Sobhan Hemati, Mahdi Beitollahi, Amir Hossein Estiri, Bassel Al Omari, Soufiane Lamghari, Yasser H. Khalil, Xi Chen, Guojun Zhang

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Beyond Text: Utilizing Vocal Cues to Improve Decision Making in LLMs for Robot Navigation Tasks Xingpeng Sun, Haoming Meng, Souradip Chakraborty, Amrit Bedi, Aniket Bera

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Bias Amplification Enhances Minority Group Performance Gaotang Li, Jiarui Liu, Wei Hu

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Bias/Variance Is Not the Same as Approximation/Estimation Gavin Brown, Riccardo Ali

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Biased Dueling Bandits with Stochastic Delayed Feedback Bongsoo Yi, Yue Kang, Yao Li

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Bit-by-Bit: Investigating the Vulnerabilities of Binary Neural Networks to Adversarial Bit Flipping Shamik Kundu, Sanjay Das, Sayar Karmakar, Arnab Raha, Souvik Kundu, Yiorgos Makris, Kanad Basu

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Blending Two Styles: Generating Inter-Domain Images with MiddleGAN Collin MacDonald, Zhendong Chu, John Stankovic, Huajie Shao, Gang Zhou, Ashley Gao

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Blind Biological Sequence Denoising with Self-Supervised Set Learning Nathan Hoyen Ng, Ji Won Park, Jae Hyeon Lee, Ryan Lewis Kelly, Stephen Ra, Kyunghyun Cho

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Blockwise Self-Supervised Learning at Scale Shoaib Siddiqui, David Krueger, Yann LeCun, Stephane Deny

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Boomerang: Local Sampling on Image Manifolds Using Diffusion Models Lorenzo Luzi, Paul M Mayer, Josue Casco-Rodriguez, Ali Siahkoohi, Richard Baraniuk

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Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration Hong Ye Tan, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb

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Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals Oliver Hahn, Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth

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BP($\mathbf{\lambda}$): Online Learning via Synthetic Gradients Joseph Oliver Pemberton, Rui Ponte Costa

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Break It, Imitate It, Fix It: Robustness by Generating Human-like Attacks Aradhana Sinha, Ananth Balashankar, Ahmad Beirami, Thi Avrahami, Jilin Chen, Alex Beutel

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Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction Seokjun Ahn, Jungtaek Kim, Minsu Cho, Jaesik Park

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Budgeted Online Model Selection and Fine-Tuning via Federated Learning Pouya M. Ghari, Yanning Shen

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Bytes Are All You Need: Transformers Operating Directly on File Bytes Maxwell Horton, Sachin Mehta, Ali Farhadi, Mohammad Rastegari

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Byzantine-Resilient Decentralized Multi-Armed Bandits Jingxuan Zhu, Alec Koppel, Alvaro Velasquez, Ji Liu

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C3DM: Constrained-Context Conditional Diffusion Models for Imitation Learning Vaibhav Saxena, Yotto Koga, Danfei Xu

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Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction Ziqi Ma, David Pitt, Kamyar Azizzadenesheli, Anima Anandkumar

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Calibrating Deep Ensemble Through Functional Variational Inference Zhijie Deng, Feng Zhou, Jianfei Chen, Guoqiang Wu, Jun Zhu

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Calibration Attacks: A Comprehensive Study of Adversarial Attacks on Model Confidence Stephen Obadinma, Xiaodan Zhu, Hongyu Guo

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Can LLMs Effectively Leverage Graph Structural Information Through Prompts, and Why? Jin Huang, Xingjian Zhang, Qiaozhu Mei, Jiaqi Ma

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Can We Count on LLMs? the Fixed-Effect Fallacy and Claims of GPT-4 Capabilities Thomas Ball, Shuo Chen, Cormac Herley

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Candidate Set Re-Ranking for Composed Image Retrieval with Dual Multi-Modal Encoder Zheyuan Liu, Weixuan Sun, Damien Teney, Stephen Gould

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CAREER: A Foundation Model for Labor Sequence Data Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David Blei

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CascadedGaze: Efficiency in Global Context Extraction for Image Restoration Amirhosein Ghasemabadi, Muhammad Kamran Janjua, Mohammad Salameh, Chunhua Zhou, Fengyu Sun, Di Niu

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Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms Daria Bystrova, Charles K. Assaad, Julyan Arbel, Emilie Devijver, Eric Gaussier, Wilfried Thuiller

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Causal Reasoning and Large Language Models: Opening a New Frontier for Causality Emre Kiciman, Robert Ness, Amit Sharma, Chenhao Tan

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Certified Deductive Reasoning with Language Models Gabriel Poesia, Kanishk Gandhi, Eric Zelikman, Noah Goodman

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Certified Robustness Against Sparse Adversarial Perturbations via Data Localization Ambar Pal, Rene Vidal, Jeremias Sulam

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CFASL: Composite Factor-Aligned Symmetry Learning for Disentanglement in Variational AutoEncoder Hee-Jun Jung, Jaehyoung Jeong, Kangil Kim

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Chain-of-Thought Unfaithfulness as Disguised Accuracy Oliver Bentham, Nathan Stringham, Ana Marasovic

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ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions Deyao Zhu, Jun Chen, Kilichbek Haydarov, Xiaoqian Shen, Wenxuan Zhang, Mohamed Elhoseiny

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Choosing the Parameter of the Fermat Distance: Navigating Geometry and Noise Frederic Chazal, Laure Ferraris, Pablo Groisman, Matthieu Jonckheere, Frederic Pascal, Facundo Fabián Sapienza

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Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization Jixuan Leng, Yijiang Li, Haohan Wang

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Chronos: Learning the Language of Time Series Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Bernie Wang

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CiPR: An Efficient Framework with Cross-Instance Positive Relations for Generalized Category Discovery Shaozhe Hao, Kai Han, Kwan-Yee K. Wong

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Class-Discriminative Attention Maps for Vision Transformers Lennart Brocki, Jakub Binda, Neo Christopher Chung

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CLIP Meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement Mohammadreza Salehi, Mehrdad Farajtabar, Maxwell Horton, Fartash Faghri, Hadi Pouransari, Raviteja Vemulapalli, Oncel Tuzel, Ali Farhadi, Mohammad Rastegari, Sachin Mehta

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CLIP-QDA: An Explainable Concept Bottleneck Model Rémi Kazmierczak, Eloïse Berthier, Goran Frehse, Gianni Franchi

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Closing the Gap Between SVRG and TD-SVRG with Gradient Splitting Arsenii Mustafin, Alex Olshevsky, Ioannis Paschalidis

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CoDeC: Communication-Efficient Decentralized Continual Learning Sakshi Choudhary, Sai Aparna Aketi, Gobinda Saha, Kaushik Roy

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Cognitive Architectures for Language Agents Theodore Sumers, Shunyu Yao, Karthik R Narasimhan, Thomas L. Griffiths

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Combine and Conquer: A Meta-Analysis on Data Shift and Out-of-Distribution Detection Eduardo Dadalto Câmara Gomes, Florence Alberge, Pierre Duhamel, Pablo Piantanida

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CoMIX: A Multi-Agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision-Making Giovanni Minelli, Mirco Musolesi

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Comparing Deterministic and Soft Policy Gradients for Optimizing Gaussian Mixture Actors Sheelabhadra Dey, Guni Sharon

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CompoDiff: Versatile Composed Image Retrieval with Latent Diffusion Geonmo Gu, Sanghyuk Chun, Wonjae Kim, HeeJae Jun, Yoohoon Kang, Sangdoo Yun

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Compositional Instruction Following with Language Models and Reinforcement Learning Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan, Steven James, Matthew Gombolay, Ray Mooney, Benjamin Rosman

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Compressing the Activation Maps in Deep Convolutional Neural Networks and Its Regularizing Effect Minh Hoang Vu, Anders Garpebring, Tufve Nyholm, Tommy Löfstedt

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Concept-Driven Continual Learning Sin-Han Yang, Tuomas Oikarinen, Tsui-Wei Weng

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Conciliator Steering: Imposing User Preference in Multi-Objective Reinforcement Learning Sara Pyykölä, Klavdiya Olegovna Bochenina, Laura Ruotsalainen

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Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning Asaf Ben Arie, Malka Gorfine

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Confidence-Aware Denoised Fine-Tuning of Off-the-Shelf Models for Certified Robustness Suhyeok Jang, Seojin Kim, Jinwoo Shin, Jongheon Jeong

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Conservative Evaluation of Offline Policy Learning Hager Radi Abdelwahed, Josiah P. Hanna, Matthew E. Taylor

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Conservative Prediction via Data-Driven Confidence Minimization Caroline Choi, Fahim Tajwar, Yoonho Lee, Huaxiu Yao, Ananya Kumar, Chelsea Finn

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ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation Weiming Ren, Huan Yang, Ge Zhang, Cong Wei, Xinrun Du, Wenhao Huang, Wenhu Chen

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Constraining Generative Models for Engineering Design with Negative Data Lyle Regenwetter, Giorgio Giannone, Akash Srivastava, Dan Gutfreund, Faez Ahmed

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Contaminated Online Convex Optimization Tomoya Kamijima, Shinji Ito

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Contextual Policies Enable Efficient and Interpretable Inverse Reinforcement Learning for Populations Ville Tanskanen, Chang Rajani, Perttu Hämäläinen, Christian Guckelsberger, Arto Klami

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Continual Adaptation of Vision Transformers for Federated Learning Shaunak Halbe, James Seale Smith, Junjiao Tian, Zsolt Kira

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Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA James Seale Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua, Zsolt Kira, Yilin Shen, Hongxia Jin

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Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning Max Vladymyrov, Andrey Zhmoginov, Mark Sandler

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Continual Learning in Open-Vocabulary Classification with Complementary Memory Systems Zhen Zhu, Weijie Lyu, Yao Xiao, Derek Hoiem

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Continual Learning: Applications and the Road Forward Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M van de Ven

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Continuous U-Net: Faster, Greater and Noiseless Chun-Wun Cheng, Christina Runkel, Lihao Liu, Raymond H. Chan, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero

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Contrastive Class Anchor Learning for Open Set Object Recognition in Driving Scenes Zizhao Li, Kourosh Khoshelham, Joseph West

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Contrastive Graph Autoencoder for Shape-Based Polygon Retrieval from Large Geometry Datasets Zexian Huang, Kourosh Khoshelham, Martin Tomko

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Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps Jakob Träuble, Lucy V Hiscox, Curtis Johnson, Carola-Bibiane Schönlieb, Gabriele S Kaminski Schierle, Angelica I Aviles-Rivero

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Contrastive Learning with Consistent Representations Zihu Wang, Yu Wang, Zhuotong Chen, Hanbin Hu, Peng Li

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Controlling Federated Learning for Covertness Adit Jain, Vikram Krishnamurthy

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Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density Shuangqi Li, Chen Liu, Tong Zhang, Hieu Le, Sabine Susstrunk, Mathieu Salzmann

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Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel’s Spectrum Amnon Geifman, Daniel Barzilai, Ronen Basri, Meirav Galun

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Convergence Analysis and Trajectory Comparison of Gradient Descent for Overparameterized Deep Linear Networks Hongru Zhao, Jinchao Xu

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Convergence Analysis of Fractional Gradient Descent Ashwani Aggarwal

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Convergences for Minimax Optimization Problems over Infinite-Dimensional Spaces Towards Stability in Adversarial Training Takashi Furuya, Satoshi Okuda, Kazuma Suetake, Yoshihide Sawada

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Cooperative Online Learning with Feedback Graphs Nicolò Cesa-Bianchi, Tommaso Cesari, Riccardo Della Vecchia

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Coordinate Transform Fourier Neural Operators for Symmetries in Physical Modelings Wenhan Gao, Ruichen Xu, Hong Wang, Yi Liu

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CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark Zachary S Siegel, Sayash Kapoor, Nitya Nadgir, Benedikt Stroebl, Arvind Narayanan

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Correcting Flaws in Common Disentanglement Metrics Louis Mahon, Lei Sha, Thomas Lukasiewicz

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Corrective Machine Unlearning Shashwat Goel, Ameya Prabhu, Philip Torr, Ponnurangam Kumaraguru, Amartya Sanyal

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Correlation Clustering with Active Learning of Pairwise Similarities Linus Aronsson, Morteza Haghir Chehreghani

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Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints Jean Vieira Alves, Diogo Leitão, Sérgio Jesus, Marco O. P. Sampaio, Javier Liébana, Pedro Saleiro, Mario A. T. Figueiredo, Pedro Bizarro

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CR-MoE: Consistent Routed Mixture-of-Experts for Scaling Contrastive Learning Ziyu Jiang, Guoqing Zheng, Yu Cheng, Ahmed Hassan Awadallah, Zhangyang Wang

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Credal Bayesian Deep Learning Michele Caprio, Souradeep Dutta, Kuk Jin Jang, Vivian Lin, Radoslav Ivanov, Oleg Sokolsky, Insup Lee

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CREW: Facilitating Human-AI Teaming Research Lingyu Zhang, Zhengran Ji, Boyuan Chen

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D3: Data Diversity Design for Systematic Generalization in Visual Question Answering Amir Rahimi, Vanessa D'Amario, Moyuru Yamada, Kentaro Takemoto, Tomotake Sasaki, Xavier Boix

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Data Attribution for Diffusion Models: Timestep-Induced Bias in Influence Estimation Tong Xie, Haoyu Li, Andrew Bai, Cho-Jui Hsieh

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Data Pruning Can Do More: A Comprehensive Data Pruning Approach for Object Re-Identification Zi Yang, Haojin Yang, Soumajit Majumder, Jorge Cardoso, Guillermo Gallego

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Data Valuation in the Absence of a Reliable Validation Set Himanshu Jahagirdar, Jiachen T. Wang, Ruoxi Jia

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Data-Centric Defense: Shaping Loss Landscape with Augmentations to Counter Model Inversion Si Chen, Feiyang Kang, Nikhil Abhyankar, Ming Jin, Ruoxi Jia

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Data-Dependent Generalization Bounds for Neural Networks with ReLU Harsh Pandey, Amitabha Bagchi, Srikanta J. Bedathur, Arindam Bhattacharya

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Dataset Distillation via Curriculum Data Synthesis in Large Data Era Zeyuan Yin, Zhiqiang Shen

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DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles Tal Daniel, Aviv Tamar

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Decentralized Decoupled Training for Federated Long-Tailed Learning Wenkai Yang, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

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Decomposition of Equivariant Maps via Invariant Maps: Application to Universal Approximation Under Symmetry. Akiyoshi Sannai, Yuuki Takai, Matthieu Cordonnier

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Deconfounding Imitation Learning with Variational Inference Risto Vuorio, Pim De Haan, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen

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Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks Stefan Bluecher, Johanna Vielhaben, Nils Strodthoff

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Deep Backtracking Counterfactuals for Causally Compliant Explanations Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach

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Deep End-to-End Causal Inference Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Agrin Hilmkil, Joel Jennings, Meyer Scetbon, Miltiadis Allamanis, Cheng Zhang

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Deep Generalized Prediction Set Classifier and Its Theoretical Guarantees Zhou Wang, Xingye Qiao

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Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions Jiayu Chen, Bhargav Ganguly, Yang Xu, Yongsheng Mei, Tian Lan, Vaneet Aggarwal

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Deep Generative Models Through the Lens of the Manifold Hypothesis: A Survey and New Connections Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh, Anthony L. Caterini, Jesse C. Cresswell

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Deep Kernel Learning of Nonlinear Latent Force Models Jacob Moss, Jeremy England, Pietro Lio

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Deep Tabular Learning via Distillation and Language Guidance Ruohan Wang, Wenhao Fu, Carlo Ciliberto

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Deep Unlearning: Fast and Efficient Gradient-Free Class Forgetting Sangamesh Kodge, Gobinda Saha, Kaushik Roy

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Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs Ahmad Naser Eddin, Jacopo Bono, David Oliveira Aparicio, Hugo Ferreira, Pedro Manuel Pinto Ribeiro, Pedro Bizarro

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DeepReShape: Redesigning Neural Networks for Efficient Private Inference Nandan Kumar Jha, Brandon Reagen

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Defending Against Unknown Corrupted Agents: Reinforcement Learning of Adversarially Robust Nash Equilibria Andi Nika, Jonathan Nöther, Adish Singla, Goran Radanovic

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Demographically-Informed Prediction Discrepancy Index: Early Warnings of Demographic Biases for Unlabeled Populations Lucas Mansilla, Estanislao Claucich, Rodrigo Echeveste, Diego H Milone, Enzo Ferrante

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Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning Maurits Bleeker, Mariya Hendriksen, Andrew Yates, Maarten de Rijke

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Demonstration-Guided Multi-Objective Reinforcement Learning Junlin Lu, Patrick Mannion, Karl Mason

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Dependency Structure Search Bayesian Optimization for Decision Making Models Mohit Rajpal, Lac Gia Tran, Yehong Zhang, Bryan Kian Hsiang Low

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Depth Scaling in Graph Neural Networks: Understanding the Flat Curve Behavior Diana Gomes, Kyriakos Efthymiadis, Ann Nowe, Peter Vrancx

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DFML: Decentralized Federated Mutual Learning Yasser H. Khalil, Amir Hossein Estiri, Mahdi Beitollahi, Nader Asadi, Sobhan Hemati, Xu Li, Guojun Zhang, Xi Chen

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Differential Equation Scaling Limits of Shaped and Unshaped Neural Networks Mufan Bill Li, Mihai Nica

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Differentially Private Kernel Inducing Points Using Features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation Margarita Vinaroz, Mijung Park

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Differentially Private Latent Diffusion Models Michael F Liu, Saiyue Lyu, Margarita Vinaroz, Mijung Park

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Differentiating Through Integer Linear Programs with Quadratic Regularization and Davis-Yin Splitting Daniel McKenzie, Howard Heaton, Samy Wu Fung

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Diffusion Models with Deterministic Normalizing Flow Priors Mohsen Zand, Ali Etemad, Michael Greenspan

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DIG in: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity Melissa Hall, Candace Ross, Adina Williams, Nicolas Carion, Michal Drozdzal, Adriana Romero-Soriano

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DIG-MILP: A Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee Haoyu Peter Wang, Jialin Liu, Xiaohan Chen, Xinshang Wang, Pan Li, Wotao Yin

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DIGNet: Learning Decomposed Patterns in Representation Balancing for Treatment Effect Estimation Yiyan Huang, Wang Siyi, Cheuk Hang Leung, Qi Wu, Dongdong Wang, Zhixiang Huang

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DINOv2: Learning Robust Visual Features Without Supervision Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski

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Directed Graph Transformers Qitong Wang, Georgios Kollias, Vasileios Kalantzis, Naoki Abe, Mohammed J Zaki

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Directional Convergence near Small Initializations and Saddles in Two-Homogeneous Neural Networks Akshay Kumar, Jarvis Haupt

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Discffusion: Discriminative Diffusion Models as Few-Shot Vision and Language Learners Xuehai He, Weixi Feng, Tsu-Jui Fu, Varun Jampani, Arjun Reddy Akula, Pradyumna Narayana, S Basu, William Yang Wang, Xin Eric Wang

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Disciplined Saddle Programming Philipp Schiele, Eric Sager Luxenberg, Stephen P. Boyd

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Discovering Model Structure of Dynamical Systems with Combinatorial Bayesian Optimization Lucas Rath, Alexander von Rohr, Andreas Schultze, Sebastian Trimpe, Burkhard Corves

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Discrete Graph Auto-Encoder Yoann Boget, Magda Gregorova, Alexandros Kalousis

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Discriminative Reconstruction via Simultaneous Dense and Sparse Coding Abiy Tasissa, Manos Theodosis, Bahareh Tolooshams, Demba E. Ba

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Distributional GFlowNets with Quantile Flows Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron Courville, Yoshua Bengio

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Distributionally Robust Policy Evaluation Under General Covariate Shift in Contextual Bandits Yihong Guo, Hao Liu, Yisong Yue, Anqi Liu

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Diversity-Preserving $k$--Armed Bandits, Revisited Hedi Hadiji, Sébastien Gerchinovitz, Jean-Michel Loubes, Gilles Stoltz

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Do Not Trust What You Trust: Miscalibration in Semisupervised Learning Shambhavi Mishra, Balamurali Murugesan, Ismail Ben Ayed, Marco Pedersoli, Jose Dolz

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Do Parameters Reveal More than Loss for Membership Inference? Anshuman Suri, Xiao Zhang, David Evans

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Does Representation Similarity Capture Function Similarity? Lucas Hayne, Heejung Jung, R. Carter

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Domain-Generalizable Multiple-Domain Clustering Amit Rozner, Barak Battash, Lior Wolf, Ofir Lindenbaum

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Double Descent and Overfitting Under Noisy Inputs and Distribution Shift for Linear Denoisers Chinmaya Kausik, Kashvi Srivastava, Rishi Sonthalia

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Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic

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DP-ImgSyn: Dataset Alignment for Obfuscated, Differentially Private Image Synthesis Efstathia Soufleri, Deepak Ravikumar, Kaushik Roy

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DrGNN: Deep Residual Graph Neural Network with Contrastive Learning Lecheng Zheng, Dongqi Fu, Ross Maciejewski, Jingrui He

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DSI2I: Dense Style for Unpaired Exemplar-Based Image-to- Image Translation Baran Ozaydin, Tong Zhang, Sabine Susstrunk, Mathieu Salzmann

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DTRNet: Precisely Correcting Selection Bias in Individual-Level Continuous Treatment Effect Estimation by Reweighted Disentangled Representation Mengxuan Hu, Zhixuan Chu, Sheng Li

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Dual-Windowed Vision Transformer with Angular Self- Attention Weili Shi, Sheng Li

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DyG2Vec: Efficient Representation Learning for Dynamic Graphs Mohammad Alomrani, Mahdi Biparva, Yingxue Zhang, Mark Coates

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DynaConF: Dynamic Forecasting of Non-Stationary Time Series Siqi Liu, Andreas Lehrmann

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Dynamic Online Ensembles of Basis Expansions Daniel Waxman, Petar Djuric

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Dynamic Structure Estimation from Bandit Feedback Using Nonvanishing Exponential Sums Motoya Ohnishi, Isao Ishikawa, Yuko Kuroki, Masahiro Ikeda

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E-Valuating Classifier Two-Sample Tests Teodora Pandeva, Tim Bakker, Christian A. Naesseth, Patrick Forré

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E(n)-Equivariant Graph Neural Cellular Automata Gennaro Gala, Daniele Grattarola, Erik Quaeghebeur

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ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text Han Yu, Peikun Guo, Akane Sano

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Effective Latent Differential Equation Models via Attention and Multiple Shooting Germán Abrevaya, Mahta Ramezanian-Panahi, Jean-Christophe Gagnon-Audet, Pablo Polosecki, Irina Rish, Silvina Ponce Dawson, Guillermo Cecchi, Guillaume Dumas

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Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty Guanlin Liu, Zhihan Zhou, Han Liu, Lifeng Lai

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Efficient Identification of Direct Causal Parents via Invariance and Minimum Error Testing Minh Nguyen, Mert R. Sabuncu

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Efficient Large Language Models: A Survey Zhongwei Wan, Xin Wang, Che Liu, Samiul Alam, Yu Zheng, Jiachen Liu, Zhongnan Qu, Shen Yan, Yi Zhu, Quanlu Zhang, Mosharaf Chowdhury, Mi Zhang

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Efficient Model-Agnostic Multi-Group Equivariant Networks Razan Baltaji, Sourya Basu, Lav R. Varshney

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Efficient Parallelized Simulation of Cyber-Physical Systems Bas van der Heijden, Laura Ferranti, Jens Kober, Robert Babuska

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EHI: End-to-End Learning of Hierarchical Index for Efficient Dense Retrieval Ramnath Kumar, Anshul Mittal, Nilesh Gupta, Aditya Kusupati, Inderjit S Dhillon, Prateek Jain

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EHRDiff : Exploring Realistic EHR Synthesis with Diffusion Models Hongyi Yuan, Songchi Zhou, Sheng Yu

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Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World Bowen Lei, Dongkuan Xu, Ruqi Zhang, Bani Mallick

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Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm Meng Liu, Haiyang Yu, Shuiwang Ji

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End-to-End Training Induces Information Bottleneck Through Layer-Role Differentiation: A Comparative Analysis with Layer-Wise Training Keitaro Sakamoto, Issei Sato

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Enhancing Compositional Generalization via Compositional Feature Alignment Haoxiang Wang, Haozhe Si, Huajie Shao, Han Zhao

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Enhancing Contrastive Clustering with Negative Pair-Guided Regularization Abhishek Kumar, Anish Chakrabarty, Sankha Subhra Mullick, Swagatam Das

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Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo Ziyi Wang, Yujie Chen, Qifan Song, Ruqi Zhang

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Enhancing Robustness to Class-Conditional Distribution Shift in Long-Tailed Recognition Keliang Li, Hong Chang, Shiguang Shan, Xilin Chen

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Enhancing Vision-Language Model with Unmasked Token Alignment Jihao Liu, Jinliang Zheng, Boxiao Liu, Yu Liu, Hongsheng Li

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Equivariant Graph Learning for High-Density Crowd Trajectories Modeling Yang Liu, Zinan Zheng, Yu Rong, Jia Li

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Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction Zhao Xu, Haiyang Yu, Montgomery Bohde, Shuiwang Ji

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Equivariant Symmetry Breaking Sets YuQing Xie, Tess Smidt

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Error Bounds for Flow Matching Methods Joe Benton, George Deligiannidis, Arnaud Doucet

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Estimating Class Separability of Text Embeddings with Persistent Homology. Kostis Gourgoulias, Najah Ghalyan, Maxime Labonne, Yash Satsangi, Sean Moran, Joseph Sabelja

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Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill

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Evaluating Graph Generative Models with Graph Kernels: What Structural Characteristics Are Captured? Martijn Gösgens, Alexey Tikhonov, Liudmila Prokhorenkova

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Evaluating Spatial Understanding of Large Language Models Yutaro Yamada, Yihan Bao, Andrew Kyle Lampinen, Jungo Kasai, Ilker Yildirim

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Evaluating the Evaluators: Are Validation Methods for Few-Shot Learning Fit for Purpose? Luísa Shimabucoro, Ruchika Chavhan, Timothy Hospedales, Henry Gouk

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Exact Fractional Inference via Re-Parametrization \& Interpolation Between Tree-Re-Weighted- and Belief Propagation- Algorithms Hamidreza Behjoo, Michael Chertkov

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Expected Pinball Loss for Quantile Regression and Inverse CDF Estimation Taman Narayan, Serena Lutong Wang, Kevin Robert Canini, Maya Gupta

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Exploit CAM by Itself: Complementary Learning System for Weakly Supervised Semantic Segmentation Wankou Yang, Jiren Mai, Fei Zhang, Tongliang Liu, Bo Han

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Exploiting Edge Features in Graph-Based Learning with Fused Network Gromov-Wasserstein Distance Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc

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Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices Frida Marie Viset, Anton Kullberg, Frederiek Wesel, Arno Solin

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Exploring Format Consistency for Instruction Tuning Shihao Liang, Runchu Tian, Kunlun Zhu, Yujia Qin, Huadong Wang, Xin Cong, Zhiyuan Liu, Xiaojiang Liu, Maosong Sun

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Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model Selection Yushu Li, Yongyi Su, Xulei Yang, Kui Jia, Xun Xu

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Exploring Simple, High Quality Out-of-Distribution Detection with L2 Normalization Jarrod Haas, William Yolland, Bernhard T Rabus

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Exploring Validation Metrics for Offline Model-Based Optimisation with Diffusion Models Christopher Beckham, Alexandre Piché, David Vazquez, Christopher Pal

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Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits Daniel Morales-Brotons, Thijs Vogels, Hadrien Hendrikx

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Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models Adyasha Maharana, Amita Kamath, Christopher Clark, Mohit Bansal, Aniruddha Kembhavi

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Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Web Hiroki Furuta, Yutaka Matsuo, Aleksandra Faust, Izzeddin Gur

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Expressive Higher-Order Link Prediction Through Hypergraph Symmetry Breaking Simon Zhang, Cheng Xin, Tamal K. Dey

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Extended Deep Submodular Functions Seyed Mohammad Hosseini, Arash Jamshidi, Seyed Mahdi Noormousavi, Mahdi Siavoshani, Naeimeh Omidvar

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Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework William Andersson, Jakob Heiss, Florian Krach, Josef Teichmann

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Extreme Risk Mitigation in Reinforcement Learning Using Extreme Value Theory Karthik Somayaji Ns, Yu Wang, Malachi Schram, Jan Drgona, Mahantesh M Halappanavar, Frank Liu, Peng Li

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Fair Feature Importance Scores for Interpreting Decision Trees Camille Olivia Little, Debolina Halder Lina, Genevera I. Allen

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Fair Representation in Submodular Subset Selection: A Pareto Optimization Approach Adriano Fazzone, Yanhao Wang, Francesco Bonchi

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Fairness Under Demographic Scarce Regime Patrik Joslin Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji

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Fast and Effective Weight Update for Pruned Large Language Models Vladimír Boža

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Fast and Expressive Gesture Recognition Using a Combination-Homomorphic Electromyogram Encoder Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger, Tales Imbiriba, Eugene Tunik, Deniz Erdogmus, Mathew Yarossi, Robin Walters

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Fast Computation of Leave-One-Out Cross-Validation for $k$-NN Regression Motonobu Kanagawa

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Fast Training of Diffusion Models with Masked Transformers Hongkai Zheng, Weili Nie, Arash Vahdat, Anima Anandkumar

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Fast, Accurate and Lightweight Sequential Simulation-Based Inference Using Gaussian Locally Linear Mappings Henrik Häggström, Pedro L. C. Rodrigues, Geoffroy Oudoumanessah, Florence Forbes, Umberto Picchini

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Faster Convergence of Local SGD for Over-Parameterized Models Tiancheng Qin, S. Rasoul Etesami, Cesar A Uribe

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Faster Optimal Univariate Microaggregation Felix I. Stamm, Michael T Schaub

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Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-Trained Models Ziting Wen, Oscar Pizarro, Stefan B. Williams

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Feature Distillation Improves Zero-Shot Transfer from Synthetic Images Niclas Popp, Jan Hendrik Metzen, Matthias Hein

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Feature Learning as Alignment: A Structural Property of Gradient Descent in Non-Linear Neural Networks Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala

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FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning Peiran Xu, Zeyu Wang, Jieru Mei, Liangqiong Qu, Alan Yuille, Cihang Xie, Yuyin Zhou

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Federated $\mathcal{X}$-Armed Bandit with Flexible Personalisation Ali Arabzadeh, James A. Grant, David S. Leslie

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Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls Saurav Prakash, Jin Sima, Chao Pan, Eli Chien, Olgica Milenkovic

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Federated Graph Learning with Graphless Clients Xingbo Fu, Song Wang, Yushun Dong, Binchi Zhang, Chen Chen, Jundong Li

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Federated Learning with Convex Global and Local Constraints Chuan He, Le Peng, Ju Sun

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Federated Learning with Reduced Information Leakage and Computation Tongxin Yin, Xuwei Tan, Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu

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Federated Sampling with Langevin Algorithm Under Isoperimetry Lukang Sun, Adil Salim, Peter Richtárik

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Federated TD Learning with Linear Function Approximation Under Environmental Heterogeneity Han Wang, Aritra Mitra, Hamed Hassani, George J. Pappas, James Anderson

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Federated Variational Inference: Towards Improved Personalization and Generalization Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield, Karan Singhal, Arash Afkanpour, Warren Richard Morningstar

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Feedback-Guided Data Synthesis for Imbalanced Classification Reyhane Askari Hemmat, Mohammad Pezeshki, Florian Bordes, Michal Drozdzal, Adriana Romero-Soriano

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Feudal Graph Reinforcement Learning Tommaso Marzi, Arshjot Singh Khehra, Andrea Cini, Cesare Alippi

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Fine-Tuning Can Cripple Your Foundation Model; Preserving Features May Be the Solution Jishnu Mukhoti, Yarin Gal, Philip Torr, Puneet K. Dokania

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Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm Semih Cayci, Niao He, R. Srikant

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Finite-Time Analysis of Temporal Difference Learning with Experience Replay Han-Dong Lim, Donghwan Lee

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Fixed Budget Best Arm Identification in Unimodal Bandits Debamita Ghosh, Manjesh Kumar Hanawal, Nikola Zlatanov

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Fixed-Budget Best-Arm Identification in Sparse Linear Bandits Recep Can Yavas, Vincent Y. F. Tan

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FlexEControl: Flexible and Efficient Multimodal Control for Text-to-Image Generation Xuehai He, Jian Zheng, Jacob Zhiyuan Fang, Robinson Piramuthu, Mohit Bansal, Vicente Ordonez, Gunnar A Sigurdsson, Nanyun Peng, Xin Eric Wang

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FLR: Label-Mixture Regularization for Federated Learning with Noisy Labels Taehyeon Kim, Donggyu Kim, Se-Young Yun

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Fooling Contrastive Language-Image Pre-Trained Models with CLIPMasterPrints Matthias Freiberger, Peter Kun, Christian Igel, Anders Sundnes Løvlie, Sebastian Risi

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For Robust Worst-Group Accuracy, Ignore Group Annotations Nathan Stromberg, Rohan Ayyagari, Monica Welfert, Sanmi Koyejo, Richard Nock, Lalitha Sankar

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Foundational Challenges in Assuring Alignment and Safety of Large Language Models Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, Jose Hernandez-Orallo, Lewis Hammond, Eric J Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Chenyu Zhang, Ruiqi Zhong, Sean O hEigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Aleksandar Petrov, Christian Schroeder de Witt, Sumeet Ramesh Motwani, Yoshua Bengio, Danqi Chen, Philip Torr, Samuel Albanie, Tegan Maharaj, Jakob Nicolaus Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger

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From Complexity to Clarity: Analytical Expressions of Deep Neural Network Weights via Clifford Algebra and Convexity Mert Pilanci

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From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond Andi Han, Dai Shi, Lequan Lin, Junbin Gao

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From Decoding to Meta-Generation: Inference-Time Algorithms for Large Language Models Sean Welleck, Amanda Bertsch, Matthew Finlayson, Hailey Schoelkopf, Alex Xie, Graham Neubig, Ilia Kulikov, Zaid Harchaoui

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From Differential Privacy to Bounds on Membership Inference: Less Can Be More Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot

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From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen

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From Persona to Personalization: A Survey on Role-Playing Language Agents Jiangjie Chen, Xintao Wang, Rui Xu, Siyu Yuan, Yikai Zhang, Wei Shi, Jian Xie, Shuang Li, Ruihan Yang, Tinghui Zhu, Aili Chen, Nianqi Li, Lida Chen, Caiyu Hu, Siye Wu, Scott Ren, Ziquan Fu, Yanghua Xiao

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From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression Xuxing Chen, Krishna Balasubramanian, Promit Ghosal, Bhavya Kumar Agrawalla

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FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance Lingjiao Chen, Matei Zaharia, James Zou

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Function Basis Encoding of Numerical Features in Factorization Machines Alex Shtoff, Elie Abboud, Rotem Stram, Oren Somekh

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Functional Linear Regression of Cumulative Distribution Functions Qian Zhang, Anuran Makur, Kamyar Azizzadenesheli

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Fundamental Problems with Model Editing: How Should Rational Belief Revision Work in LLMs? Peter Hase, Thomas Hofweber, Xiang Zhou, Elias Stengel-Eskin, Mohit Bansal

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G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks Zhaoyu Li, Jinpei Guo, Xujie Si

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Gaussian-Smoothed Sliced Probability Divergences Mokhtar Z. Alaya, Alain Rakotomamonjy, Maxime Berar, Gilles Gasso

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GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data Andrei Margeloiu, Nikola Simidjievski, Pietro Lio, Mateja Jamnik

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Generalization Bounds with Logarithmic Negative-Sample Dependence for Adversarial Contrastive Learning Naghmeh Ghanooni, Waleed Mustafa, Yunwen Lei, Anthony Widjaja Lin, Marius Kloft

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Generalized Oversampling for Learning from Imbalanced Datasets and Associated Theory: Application in Regression Samuel Stocksieker, Denys Pommeret, Arthur Charpentier

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Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields Yi-Lun Liao, Tess Smidt, Muhammed Shuaibi, Abhishek Das

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Generalizing Neural Additive Models via Statistical Multimodal Analysis Young Kyung Kim, Juan Matias Di Martino, Guillermo Sapiro

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Generating Less Certain Adversarial Examples Improves Robust Generalization Minxing Zhang, Michael Backes, Xiao Zhang

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Generating with Confidence: Uncertainty Quantification for Black-Box Large Language Models Zhen Lin, Shubhendu Trivedi, Jimeng Sun

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Generative Models Are Self-Watermarked: Declaring Model Authentication Through Re-Generation Aditya Desu, Xuanli He, Qiongkai Xu, Wei Lu

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Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies Yaochen Xie, Ziqian Xie, Sheikh Muhammad Saiful Islam, Degui Zhi, Shuiwang Ji

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Geometrical Aspects of Lattice Gauge Equivariant Convolutional Neural Networks David I. Müller, Jimmy Aronsson, Daniel Schuh

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GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data Xinwei Zhang, Mingyi Hong, Jie Chen

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Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries Swetha Ganesh, Jiayu Chen, Gugan Thoppe, Vaneet Aggarwal

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Global Convergence of SGD for Logistic Loss on Two Layer Neural Nets Pulkit Gopalani, Samyak Jha, Anirbit Mukherjee

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GOPlan: Goal-Conditioned Offline Reinforcement Learning by Planning with Learned Models Mianchu Wang, Rui Yang, Xi Chen, Hao Sun, Meng Fang, Giovanni Montana

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Gradient Scarcity in Graph Learning with Bilevel Optimization Hashem Ghanem, Samuel Vaiter, Nicolas Keriven

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Gradient-Guided Discrete Walk-Jump Sampling for Biological Sequence Generation Zarif Ikram, Dianbo Liu, M Saifur Rahman

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Granger Causal Interaction Skill Chains Caleb Chuck, Kevin Black, Aditya Arjun, Yuke Zhu, Scott Niekum

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Graph Cuts with Arbitrary Size Constraints Through Optimal Transport Chakib Fettal, Lazhar Labiod, Mohamed Nadif

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Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data Mengxi Wu, Mohammad Rostami

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Graph Knowledge Distillation to Mixture of Experts Pavel Rumiantsev, Mark Coates

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Graph Neural Networks Formed via Layer-Wise Ensembles of Heterogeneous Base Models Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf

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Graph Pooling via Ricci Flow Amy Feng, Melanie Weber

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Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi

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Graph Structure Learning with Interpretable Bayesian Neural Networks Max Wasserman, Gonzalo Mateos

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GraphMaker: Can Diffusion Models Generate Large Attributed Graphs? Mufei Li, Eleonora Kreacic, Vamsi K. Potluru, Pan Li

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Graphon-Explainer: Generating Model-Level Explanations for Graph Neural Networks Using Graphons Sayan Saha, Sanghamitra Bandyopadhyay

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GraphPrivatizer: Improved Structural Differential Privacy for Graph Neural Networks Rucha Bhalchandra Joshi, Patrick Indri, Subhankar Mishra

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Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models Cristina Nader Vasconcelos, Abdullah Rashwan, Austin Waters, Trevor Walker, Keyang Xu, Jimmy Yan, Rui Qian, Yeqing Li, Shixin Luo, Yasumasa Onoe, Zarana Parekh, Ivana Kajic, Mandy Guo, Wenlei Zhou, Sarah Rosston, Roopal Garg, Hongliang Fei, Jordi Pont-Tuset, Su Wang, Henna Nandwani, Andrew Bunner, Kevin Swersky, David J. Fleet, Oliver Wang, Jason Michael Baldridge

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Grid Cell-Inspired Fragmentation and Recall for Efficient mAP Building Jaedong Hwang, Zhang-Wei Hong, Eric R Chen, Akhilan Boopathy, Pulkit Agrawal, Ila R Fiete

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Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity Jack William Miller, Charles O'Neill, Thang D Bui

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Gromov-Wasserstein-like Distances in the Gaussian Mixture Models Space Antoine Salmona, Agnes Desolneux, Julie Delon

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Group Fairness in Reinforcement Learning via Multi-Objective Rewards Jack Blandin, Ian A. Kash

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Growing Tiny Networks: Spotting Expressivity Bottlenecks and Fixing Them Optimally Manon Verbockhaven, Théo Rudkiewicz, Sylvain Chevallier, Guillaume Charpiat

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GSURE-Based Diffusion Model Training with Corrupted Data Bahjat Kawar, Noam Elata, Tomer Michaeli, Michael Elad

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Guarantees of Confidentiality via Hammersley-Chapman-Robbins Bounds Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert

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GUARD: A Safe Reinforcement Learning Benchmark Weiye Zhao, Yifan Sun, Feihan Li, Rui Chen, Ruixuan Liu, Tianhao Wei, Changliu Liu

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Harnessing the Power of Federated Learning in Federated Contextual Bandits Chengshuai Shi, Ruida Zhou, Kun Yang, Cong Shen

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Hashing with Uncertainty Quantification via Sampling-Based Hypothesis Testing Yucheng Wang, Mingyuan Zhou, Xiaoning Qian

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Hessian Free Efficient Single Loop Iterative Differentiation Methods for Bi-Level Optimization Problems Peiran Yu, Junyi Li, Heng Huang

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Heterogeneous Graph Adaptive Flow Network Lu Yiqi, Feng Ji, Wee Peng Tay

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Hierarchical Neural Simulation-Based Inference over Event Ensembles Lukas Heinrich, Siddharth Mishra-Sharma, Chris Pollard, Philipp Windischhofer

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Hierarchical VAE with a Diffusion-Based VampPrior Anna Kuzina, Jakub M. Tomczak

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Hierarchically Branched Diffusion Models Leverage Dataset Structure for Class-Conditional Generation Alex M Tseng, Max W Shen, Tommaso Biancalani, Gabriele Scalia

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HiFE: Hierarchical Feature Ensemble Framework for Few-Shot Hypotheses Adaptation Yongfeng Zhong, Haoang Chi, Feng Liu, Xiao-Ming Wu, Bo Han

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High-Dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy Lam Ngo, Huong Ha, Jeffrey Chan, Vu Nguyen, Hongyu Zhang

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Holistic Molecular Representation Learning via Multi-View Fragmentation Seojin Kim, Jaehyun Nam, Junsu Kim, Hankook Lee, Sungsoo Ahn, Jinwoo Shin

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Homogenizing Non-IID Datasets via In-Distribution Knowledge Distillation for Decentralized Learning Deepak Ravikumar, Gobinda Saha, Sai Aparna Aketi, Kaushik Roy

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How Does Over-Squashing Affect the Power of GNNs? Francesco Di Giovanni, T. Konstantin Rusch, Michael Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Veličković

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How Far Are We from AGI: Are LLMs All We Need? Tao Feng, Chuanyang Jin, Jingyu Liu, Kunlun Zhu, Haoqin Tu, Zirui Cheng, Guanyu Lin, Jiaxuan You

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How Good Is Good-Turing for Markov Samples? Prafulla Chandra, Andrew Thangaraj, Nived Rajaraman

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How Much Pre-Training Is Enough to Discover a Good Subnetwork? Cameron R. Wolfe, Fangshuo Liao, Qihan Wang, Junhyung Lyle Kim, Anastasios Kyrillidis

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How to Choose the Right Transfer Learning Protocol? a Qualitative Analysis in a Controlled Set-up Federica Gerace, Diego Doimo, Stefano Sarao Mannelli, Luca Saglietti, Alessandro Laio

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How to Think Step-by-Step: A Mechanistic Understanding of Chain-of-Thought Reasoning Subhabrata Dutta, Joykirat Singh, Soumen Chakrabarti, Tanmoy Chakraborty

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HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes Yuhta Takida, Yukara Ikemiya, Takashi Shibuya, Kazuki Shimada, Woosung Choi, Chieh-Hsin Lai, Naoki Murata, Toshimitsu Uesaka, Kengo Uchida, Wei-Hsiang Liao, Yuki Mitsufuji

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Hybrid Active Learning with Uncertainty-Weighted Embeddings Yinan He, Lile Cai, Jingyi Liao, Chuan-Sheng Foo

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Hybrid Federated Learning for Feature & Sample Heterogeneity: Algorithms and Implementation Xinwei Zhang, Wotao Yin, Mingyi Hong, Tianyi Chen

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Hyper-Parameter Tuning for Fair Classification Without Sensitive Attribute Access Akshaj Kumar Veldanda, Ivan Brugere, Sanghamitra Dutta, Alan Mishler, Siddharth Garg

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Hyperbolic Random Forests Lars Doorenbos, Pablo Márquez Neila, Raphael Sznitman, Pascal Mettes

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Hyperspherical Prototype Node Clustering Jitao Lu, Danyang Wu, Feiping Nie, Rong Wang, Xuelong Li

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Identifiable Causal Inference with Noisy Treatment and No Side Information Antti Pöllänen, Pekka Marttinen

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Identify Ambiguous Tasks Combining Crowdsourced Labels by Weighting Areas Under the Margin Tanguy Lefort, Benjamin Charlier, Alexis Joly, Joseph Salmon

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Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis Chiu-Chou Lin, Yu-Wei Shih, Kuei-Ting Kuo, Yu-Cheng Chen, Chien-Hua Chen, Wei-Chen Chiu, I-Chen Wu

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iHyperTime: Interpretable Time Series Generation with Implicit Neural Representations Elizabeth Fons, Alejandro Sztrajman, Yousef El-Laham, Andrea Coletta, Alexandros Iosifidis, Svitlana Vyetrenko

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IM-Context: In-Context Learning for Imbalanced Regression Tasks Ismail Nejjar, Faez Ahmed, Olga Fink

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Image Reconstruction via Deep Image Prior Subspaces Riccardo Barbano, Javier Antoran, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta

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IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual Learning Prashant Shivaram Bhat, Bharath Chennamkulam Renjith, Elahe Arani, Bahram Zonooz

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Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction Jiayang Shi, Junyi Zhu, Daniel Pelt, Joost Batenburg, Matthew B. Blaschko

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Implicit Regularization of AdaDelta Matthias Englert, Ranko Lazic, Avi Semler

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IMProv: Inpainting-Based Multimodal Prompting for Computer Vision Tasks Jiarui Xu, Yossi Gandelsman, Amir Bar, Jianwei Yang, Jianfeng Gao, Trevor Darrell, Xiaolong Wang

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Improve Certified Training with Signal-to-Noise Ratio Loss to Decrease Neuron Variance and Increase Neuron Stability Tianhao Wei, Ziwei Wang, Peizhi Niu, Abulikemu Abuduweili, Weiye Zhao, Casidhe Hutchison, Eric Sample, Changliu Liu

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Improved Convergence of Score-Based Diffusion Models via Prediction-Correction Francesco Pedrotti, Jan Maas, Marco Mondelli

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Improved Motif-Scaffolding with SE(3) Flow Matching Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, Jose Jimenez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noe, Regina Barzilay, Tommi Jaakkola

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Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization Fang Kong, XiangCheng Zhang, Baoxiang Wang, Shuai Li

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Improved Variational Bayesian Phylogenetic Inference Using Mixtures Ricky Molén, Oskar Kviman, Jens Lagergren

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Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport Alexander Tong, Kilian Fatras, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Guy Wolf, Yoshua Bengio

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Improving Black-Box Robustness with In-Context Rewriting Kyle O'Brien, Nathan Hoyen Ng, Isha Puri, Jorge Mendez-Mendez, Hamid Palangi, Yoon Kim, Marzyeh Ghassemi, Thomas Hartvigsen

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Improving Diffusion Models for Scene Text Editing with Dual Encoders Jiabao Ji, Guanhua Zhang, Zhaowen Wang, Bairu Hou, Zhifei Zhang, Brian L. Price, Shiyu Chang

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Improving Generalization of Complex Models Under Unbounded Loss Using PAC-Bayes Bounds Xitong Zhang, Avrajit Ghosh, Guangliang Liu, Rongrong Wang

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Improving Predictor Reliability with Selective Recalibration Thomas P Zollo, Zhun Deng, Jake Snell, Toniann Pitassi, Richard Zemel

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Improving Robust Generalization with Diverging Spanned Latent Space Owen Dou, Zhiqiang Gao, Hangchi Shen, Ziling Yuan, Shufei Zhang, Kaizhu Huang

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Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings Nurudin Alvarez-Gonzalez, Andreas Kaltenbrunner, Vicenç Gómez

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Improving Text-to-Image Consistency via Automatic Prompt Optimization Oscar Mañas, Pietro Astolfi, Melissa Hall, Candace Ross, Jack Urbanek, Adina Williams, Aishwarya Agrawal, Adriana Romero-Soriano, Michal Drozdzal

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Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families Vaidotas Simkus, Michael U. Gutmann

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In-Context Learning with Retrieved Demonstrations for Language Models: A Survey Man Luo, Xin Xu, Yue Liu, Panupong Pasupat, Mehran Kazemi

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Incorporating Inductive Biases to Energy-Based Generative Models Yukun Li, Liping Liu

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Incorporating Prior Knowledge into Neural Networks Through an Implicit Composite Kernel Ziyang Jiang, Tongshu Zheng, Yiling Liu, David Carlson

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Incorporating Unlabelled Data into Bayesian Neural Networks Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin

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Incremental Extractive Opinion Summarization Using Cover Trees Somnath Basu Roy Chowdhury, Nicholas Monath, Kumar Avinava Dubey, Manzil Zaheer, Andrew McCallum, Amr Ahmed, Snigdha Chaturvedi

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Incremental Spatial and Spectral Learning of Neural Operators for Solving Large-Scale PDEs Robert Joseph George, Jiawei Zhao, Jean Kossaifi, Zongyi Li, Anima Anandkumar

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Independence Testing for Temporal Data Cencheng Shen, Jaewon Chung, Ronak Mehta, Ting Xu, Joshua T Vogelstein

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Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits Jie Bian, Vincent Y. F. Tan

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InduCE: Inductive Counterfactual Explanations for Graph Neural Networks Samidha Verma, Burouj Armgaan, Sourav Medya, Sayan Ranu

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Inductive Global and Local Manifold Approximation and Projection Jungeum Kim, Xiao Wang

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Inference from Real-World Sparse Measurements Arnaud Pannatier, Kyle Matoba, François Fleuret

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InfoNCE Is Variational Inference in a Recognition Parameterised Model Laurence Aitchison, Stoil Krasimirov Ganev

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InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers Leonid Boytsov, Preksha Patel, Vivek Sourabh, Riddhi Nisar, Sayani Kundu, Ramya Ramanathan, Eric Nyberg

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Input Normalized Stochastic Gradient Descent Training for Deep Neural Networks Salih Furkan Atici, Hongyi Pan, Ahmet Cetin

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INSPIRE: Incorporating Diverse Feature Preferences in Recourse Prateek Yadav, Peter Hase, Mohit Bansal

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Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes Talay M Cheema, Carl Edward Rasmussen

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Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters Marloes Arts, Jes Frellsen, Wouter Boomsma

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Interpretable Additive Tabular Transformer Networks Anton Frederik Thielmann, Arik Reuter, Thomas Kneib, David Rügamer, Benjamin Säfken

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Interpreting CLIP: Insights on the Robustness to ImageNet Distribution Shifts Jonathan Crabbé, Pau Rodriguez, Vaishaal Shankar, Luca Zappella, Arno Blaas

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Interpreting Global Perturbation Robustness of Image Models Using Axiomatic Spectral Importance Decomposition Roisin Luo, James McDermott, Colm O'Riordan

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Intriguing Properties of Hyperbolic Embeddings in Vision-Language Models Sarah Ibrahimi, Mina Ghadimi Atigh, Nanne Van Noord, Pascal Mettes, Marcel Worring

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Introducing "Forecast Utterance" for Conversational Data Science Md. Mahadi Hassan, Alex Knipper, Shubhra Kanti Karmaker Santu

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Introspective Experience Replay: Look Back When Surprised Ramnath Kumar, Dheeraj Mysore Nagaraj

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Invariance & Causal Representation Learning: Prospects and Limitations Simon Bing, Tom Hochsprung, Jonas Wahl, Urmi Ninad, Jakob Runge

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InvariantStock: Learning Invariant Features for Mastering the Shifting Market Haiyao Cao, Jinan Zou, Yuhang Liu, Zhen Zhang, Ehsan Abbasnejad, Anton van den Hengel, Javen Qinfeng Shi

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Inverse Kernel Decomposition Chengrui Li, Anqi Wu

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IRWE: Inductive Random Walk for Joint Inference of Identity and Position Network Embedding Meng Qin, Dit-Yan Yeung

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Is Value Functions Estimation with Classification Plug-and- Play for Offline Reinforcement Learning? Denis Tarasov, Kirill Brilliantov, Dmitrii Kharlapenko

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ITEM: Improving Training and Evaluation of Message-Passing Based GNNs for Top-K Recommendation Yannis Karmim, Elias Ramzi, Raphael Fournier-S'niehotta, Nicolas Thome

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Jigsaw Game: Federated Clustering Jinxuan Xu, Hong-You Chen, Wei-Lun Chao, Yuqian Zhang

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KD-BIRL: Kernel Density Bayesian Inverse Reinforcement Learning Aishwarya Mandyam, Didong Li, Andrew Jones, Diana Cai, Barbara E Engelhardt

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Kernel Normalized Convolutional Networks Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Daniel Rueckert, Georgios Kaissis

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kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies Zhongrui Gui, Shuyang Sun, Runjia Li, Jianhao Yuan, Zhaochong An, Karsten Roth, Ameya Prabhu, Philip Torr

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Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting Timm Hess, Eli Verwimp, Gido M van de Ven, Tinne Tuytelaars

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Koopman Spectrum Nonlinear Regulators and Efficient Online Learning Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham M. Kakade, Yoshinobu Kawahara

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Language Models Are Better than Humans at Next-Token Prediction Buck Shlegeris, Fabien Roger, Lawrence Chan, Euan McLean

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Language Models Speed up Local Search for Finding Programmatic Policies Quazi Asif Sadmine, Hendrik Baier, Levi Lelis

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Large Language Models (LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey Xi Fang, Weijie Xu, Fiona Anting Tan, Ziqing Hu, Jiani Zhang, Yanjun Qi, Srinivasan H. Sengamedu, Christos Faloutsos

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Large Language Models Can Be Guided to Evade AI-Generated Text Detection Ning Lu, Shengcai Liu, Rui He, Yew-Soon Ong, Qi Wang, Ke Tang

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Large Language Models Synergize with Automated Machine Learning Jinglue Xu, Jialong Li, Zhen Liu, Nav Suryanarayanan, Guoyuan Zhou, Jia Guo, Hitoshi Iba, Kenji Tei

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Large-Width Asymptotics and Training Dynamics of $\alpha$-Stable ReLU Neural Networks Stefano Favaro, Sandra Fortini, Stefano Peluchetti

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Layer-Diverse Negative Sampling for Graph Neural Networks Wei Duan, Jie Lu, Yu Guang Wang, Junyu Xuan

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Layerwise Complexity-Matched Learning Yields an Improved Model of Cortical Area V2 Nikhil Parthasarathy, Olivier J Henaff, Eero P Simoncelli

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LEA: Learning Latent Embedding Alignment Model for fMRI Decoding and Encoding Xuelin Qian, Yikai Wang, Xinwei Sun, Yanwei Fu, Xiangyang Xue, Jianfeng Feng

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LeanVec: Searching Vectors Faster by Making Them Fit Mariano Tepper, Ishwar Singh Bhati, Cecilia Aguerrebere, Mark Hildebrand, Theodore L. Willke

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Learned Feature Representations Are Biased by Complexity, Learning Order, Position, and More Andrew Kyle Lampinen, Stephanie C.Y. Chan, Katherine Hermann

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Learning $k$-Level Structured Sparse Neural Networks Using Group Envelope Regularization Yehonathan Refael, Iftach Arbel, Wasim Huleihel

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Learning a Decision Tree Algorithm with Transformers Yufan Zhuang, Liyuan Liu, Chandan Singh, Jingbo Shang, Jianfeng Gao

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Learning by Self-Explaining Wolfgang Stammer, Felix Friedrich, David Steinmann, Manuel Brack, Hikaru Shindo, Kristian Kersting

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Learning Counterfactually Invariant Predictors Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus

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Learning from Natural Language Feedback Angelica Chen, Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Samuel R. Bowman, Kyunghyun Cho, Ethan Perez

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Learning Hierarchical Relational Representations Through Relational Convolutions Awni Altabaa, John Lafferty

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Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods Julien Ferry, Gabriel Laberge, Ulrich Aïvodji

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Learning Multi-Modal Generative Models with Permutation-Invariant Encoders and Tighter Variational Objectives Marcel Hirt, Domenico Campolo, Victoria Leong, Juan-Pablo Ortega

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Learning Network Granger Causality Using Graph Prior Knowledge Lucas Zoroddu, Pierre Humbert, Laurent Oudre

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Learning Sparse Graphs for Functional Regression Using Graph-Induced Operator-Valued Kernels Akash Saha, Balamurugan Palaniappan

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Learning State Reachability as a Graph in Translation Invariant Goal-Based Reinforcement Learning Tasks Hedwin Bonnavaud, Alexandre Albore, Emmanuel Rachelson

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Learning Sub-Second Routing Optimization in Computer Networks Requires Packet-Level Dynamics Andreas Boltres, Niklas Freymuth, Patrick Jahnke, Holger Karl, Gerhard Neumann

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Learning the Essential in Less than 2k Additional Weights - A Simple Approach to Improve Image Classification Stability Under Corruptions Kai Bäuerle, Patrick Müller, Syed Muhammad Kazim, Ivo Ihrke, Margret Keuper

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Learning to Abstain from Uninformative Data Yikai Zhang, Songzhu Zheng, Mina Dalirrooyfard, Pengxiang Wu, Anderson Schneider, Anant Raj, Yuriy Nevmyvaka, Chao Chen

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Learning Tree-Structured Composition of Data Augmentation Dongyue Li, Kailai Chen, Predrag Radivojac, Hongyang R. Zhang

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Learning Under Imitative Strategic Behavior with Unforeseeable Outcomes Tian Xie, Zhiqun Zuo, Mohammad Mahdi Khalili, Xueru Zhang

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Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation Marawan Elbatel, Hualiang Wang, Jixiang Chen, Hao Wang, Xiaomeng Li

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Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs Tim Postuvan, Claas Grohnfeldt, Michele Russo, Giulio Lovisotto

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LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations Mohammad Alkhalefi, Georgios Leontidis, Mingjun Zhong

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Let There Be Direction in Hypergraph Neural Networks Stefano Fiorini, Stefano Coniglio, Michele Ciavotta, Alessio Del Bue

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Leveraging Endo- and Exo-Temporal Regularization for Black-Box Video Domain Adaptation Yuecong Xu, Jianfei Yang, Haozhi Cao, Min Wu, Xiaoli Li, Lihua Xie, Zhenghua Chen

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Leveraging Function Space Aggregation for Federated Learning at Scale Nikita Dhawan, Nicole Elyse Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite

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Leveraging Task Structures for Improved Identifiability in Neural Network Representations Wenlin Chen, Julien Horwood, Juyeon Heo, José Miguel Hernández-Lobato

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Linear Bandits with Memory Giulia Clerici, Pierre Laforgue, Nicolò Cesa-Bianchi

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Linear Weight Interpolation Leads to Transient Performance Gains Gaurav Iyer, Gintare Karolina Dziugaite, David Rolnick

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LInK: Learning Joint Representations of Design and Performance Spaces Through Contrastive Learning for Mechanism Synthesis Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Kai Xu, Faez Ahmed

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LINOCS: Lookahead Inference of Networked Operators for Continuous Stability Noga Mudrik, Eva Yezerets, Yenho Chen, Christopher John Rozell, Adam Shabti Charles

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LLM-Grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models Long Lian, Boyi Li, Adam Yala, Trevor Darrell

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LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-Based Representations Yudong Xu, Wenhao Li, Pashootan Vaezipoor, Scott Sanner, Elias Boutros Khalil

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Locally Adaptive Federated Learning Sohom Mukherjee, Nicolas Loizou, Sebastian U Stich

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Lookahead Counterfactual Fairness Zhiqun Zuo, Tian Xie, Xuwei Tan, Xueru Zhang, Mohammad Mahdi Khalili

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LoRA Learns Less and Forgets Less Dan Biderman, Jacob Portes, Jose Javier Gonzalez Ortiz, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, John Patrick Cunningham

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Low-Rank Tensor-Network Encodings for Video-to-Action Behavioral Cloning Brian Chen, Doruk Aksoy, David J Gorsich, Shravan Veerapaneni, Alex Gorodetsky

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Lyra: Orchestrating Dual Correction in Automated Theorem Proving Chuanyang Zheng, Haiming Wang, Enze Xie, Zhengying Liu, Jiankai Sun, Huajian Xin, Jianhao Shen, Zhenguo Li, Yu Li

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M$^3$PL: Identifying and Exploiting View Bias of Prompt Learning Chujie Zhao, Tianren Zhang, Guanyu Chen, Yizhou Jiang, Feng Chen

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MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Neural Networks Charles Arnal, Felix Hensel, Mathieu Carrière, Théo Lacombe, Hiroaki Kurihara, Yuichi Ike, Frederic Chazal

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Making Translators Privacy-Aware on the User's Side Ryoma Sato

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Manifold Contrastive Learning with Variational Lie Group Operators Kion Fallah, Alec Helbling, Kyle A. Johnsen, Christopher John Rozell

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Mantis: Interleaved Multi-Image Instruction Tuning Dongfu Jiang, Xuan He, Huaye Zeng, Cong Wei, Max Ku, Qian Liu, Wenhu Chen

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MaskBit: Embedding-Free Image Generation via Bit Tokens Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel Cremers, Liang-Chieh Chen

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Masked Autoencoders Are PDE Learners Anthony Zhou, Amir Barati Farimani

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Masked Multi-Prediction for Multi-Aspect Anomaly Detection Yassine Naji, Romaric Audigier, Aleksandr Setkov, Angelique Loesch, Michèle Gouiffès

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MaskMA: Towards Zero-Shot Multi-Agent Decision Making with Mask-Based Collaborative Learning Jie Liu, Yinmin Zhang, Chuming Li, Zhiyuan You, Zhanhui Zhou, Chao Yang, Yaodong Yang, Yu Liu, Wanli Ouyang

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MaskOCR: Scene Text Recognition with Masked Vision-Language Pre-Training Pengyuan Lyu, Chengquan Zhang, Shanshan Liu, Meina Qiao, Yangliu Xu, Liang Wu, Kun Yao, Junyu Han, Errui Ding, Jingdong Wang

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Maximizing Global Model Appeal in Federated Learning Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi

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MC Layer Normalization for Calibrated Uncertainty in Deep Learning Thomas Frick, Diego Antognini, Ioana Giurgiu, Benjamin F Grewe, Cristiano Malossi, Rong J.B. Zhu, Mattia Rigotti

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MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path Qian Wang, Biao Zhang, Michael Birsak, Peter Wonka

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Measuring Orthogonality in Representations of Generative Models Robin C. Geyer, Alessandro Torcinovich, João B. S. Carvalho, Alexander Meyer, Joachim M. Buhmann

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Mechanistic Interpretability for AI Safety - A Review Leonard Bereska, Stratis Gavves

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Membership Inference Attacks and Privacy in Topic Modeling Nico Manzonelli, Wanrong Zhang, Salil Vadhan

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Memorisation in Machine Learning: A Survey of Results Dmitrii Usynin, Moritz Knolle, Georgios Kaissis

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Merging by Matching Models in Task Parameter Subspaces Derek Tam, Mohit Bansal, Colin Raffel

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Merging Text Transformer Models from Different Initializations Neha Verma, Maha Elbayad

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MESSY Estimation: Maximum-Entropy Based Stochastic and Symbolic densitY Estimation Tony Tohme, Mohsen Sadr, Kamal Youcef-Toumi, Nicolas Hadjiconstantinou

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Meta Learning for Support Recovery of High-Dimensional Ising Models Huiming Xie, Jean Honorio

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Meta-Learning Approach for Joint Multimodal Signals with Multimodal Iterative Adaptation Sehun Lee, Wonkwang Lee, Gunhee Kim

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Meta-Learning Under Task Shift Lei Sun, Yusuke Tanaka, Tomoharu Iwata

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Mildly Constrained Evaluation Policy for Offline Reinforcement Learning Linjie Xu, Zhengyao Jiang, Jinyu Wang, Lei Song, Jiang Bian

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Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape Kedar Karhadkar, Michael Murray, Hanna Tseran, Guido Montufar

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Mind the Truncation Gap: Challenges of Learning on Dynamic Graphs with Recurrent Architectures João Bravo, Jacopo Bono, Hugo Ferreira, Pedro Saleiro, Pedro Bizarro

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Mini-Batch Optimization of Contrastive Loss Jaewoong Cho, Kartik Sreenivasan, Keon Lee, Kyunghoo Mun, Soheun Yi, Jeong-Gwan Lee, Anna Lee, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook Lee

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Mislabeled Examples Detection Viewed as Probing Machine Learning Models: Concepts, Survey and Extensive Benchmark Thomas George, Pierre Nodet, Alexis Bondu, Vincent Lemaire

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Misspecification-Robust Sequential Neural Likelihood for Simulation-Based Inference Ryan P. Kelly, David J Nott, David Tyler Frazier, David J Warne, Christopher Drovandi

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Mitigating Group Bias in Federated Learning: Beyond Local Fairness Ganghua Wang, Ali Payani, Myungjin Lee, Ramana Rao Kompella

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Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-Learning: A Novel Correction Approach Baturay Saglam, Doğan Can Çiçek, Furkan Burak Mutlu, Suleyman Kozat

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Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Ting Zhu, Yue Jin, Jeremie Houssineau, Giovanni Montana

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Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness Bhavya Vasudeva, Kameron Shahabi, Vatsal Sharan

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Mixed Nash for Robust Federated Learning Wanyun Xie, Thomas Pethick, Ali Ramezani-Kebrya, Volkan Cevher

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MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers Yatong Bai, Mo Zhou, Vishal M. Patel, Somayeh Sojoudi

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Mixture of Latent Experts Using Tensor Products Zhan Su, Fengran Mo, Prayag Tiwari, Benyou Wang, Qiuchi Li, Jian-Yun Nie, Jakob Grue Simonsen

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MMD-Regularized Unbalanced Optimal Transport Piyushi Manupriya, SakethaNath Jagarlapudi, Pratik Jawanpuria

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MOCA: Self-Supervised Representation Learning by Predicting Masked Online Codebook Assignments Spyros Gidaris, Andrei Bursuc, Oriane Siméoni, Antonín Vobecký, Nikos Komodakis, Matthieu Cord, Patrick Perez

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MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection Kemal Oksuz, Selim Kuzucu, Tom Joy, Puneet K. Dokania

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Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries Patrick Chao, Patrick Blöbaum, Sapan Kirit Patel, Shiva Kasiviswanathan

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Models of Human Preference for Learning Reward Functions W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, Alessandro G Allievi

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Modular Federated Contrastive Learning with Twin Normalization for Resource-Limited Clients Azadeh Motamedi, Il Min Kim

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Modular Quantization-Aware Training for 6d Object Pose Estimation Saqib Javed, Chengkun Li, Andrew Lawrence Price, Yinlin Hu, Mathieu Salzmann

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ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers Junjie Yin, Jiahao Dong, Yingheng Wang, Christopher De Sa, Volodymyr Kuleshov

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MoMA: Model-Based Mirror Ascent for Offline Reinforcement Learning Mao Hong, Zhiyue Zhang, Yue Wu, Yanxun Xu

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Momentum-Based Policy Gradient with Second-Order Information Saber Salehkaleybar, Mohammadsadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran

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More Agents Is All You Need Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu, Deheng Ye

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MUBen: Benchmarking the Uncertainty of Molecular Representation Models Yinghao Li, Lingkai Kong, Yuanqi Du, Yue Yu, Yuchen Zhuang, Wenhao Mu, Chao Zhang

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Multi-Accurate CATE Is Robust to Unknown Covariate Shifts Christoph Kern, Michael P. Kim, Angela Zhou

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Multi-Conditioned Graph Diffusion for Neural Architecture Search Rohan Asthana, Joschua Conrad, Youssef Dawoud, Maurits Ortmanns, Vasileios Belagiannis

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Multi-Fidelity Active Learning with GFlowNets Alex Hernández-García, Nikita Saxena, Moksh Jain, Cheng-Hao Liu, Yoshua Bengio

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Multi-Grid Tensorized Fourier Neural Operator for High- Resolution PDEs Jean Kossaifi, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Anima Anandkumar

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Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning Trevor McInroe, Lukas Schäfer, Stefano V Albrecht

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Multi-Intention Inverse Q-Learning for Interpretable Behavior Representation Hao Zhu, Brice De La Crompe, Gabriel Kalweit, Artur Schneider, Maria Kalweit, Ilka Diester, Joschka Boedecker

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Multi-LoRA Composition for Image Generation Ming Zhong, Yelong Shen, Shuohang Wang, Yadong Lu, Yizhu Jiao, Siru Ouyang, Donghan Yu, Jiawei Han, Weizhu Chen

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Multimodal Chain-of-Thought Reasoning in Language Models Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, Alex Smola

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Multiple Kronecker RLS Fusion-Based Link Propagation for Drug-Side Effect Prediction Yuqing Qian, Ziyu Zheng, Prayag Tiwari, Yijie Ding, Quan Zou

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Multitask Learning Can Improve Worst-Group Outcomes Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig

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Navigating Noise: A Study of How Noise Influences Generalisation and Calibration of Neural Networks Martin Ferianc, Ondrej Bohdal, Timothy Hospedales, Miguel R. D. Rodrigues

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Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures Vincent Abbott

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Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration Yung-Chen Tang, Pin-Yu Chen, Tsung-Yi Ho

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Neural Graph Reasoning: A Survey on Complex Logical Query Answering Hongyu Ren, Mikhail Galkin, Zhaocheng Zhu, Jure Leskovec, Michael Cochez

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Neural Implicit Manifold Learning for Topology-Aware Density Estimation Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell

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Neural Incomplete Factorization: Learning Preconditioners for the Conjugate Gradient Method Paul Häusner, Ozan Öktem, Jens Sjölund

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Neural Likelihood Approximation for Integer Valued Time Series Data Luke O'Loughlin, Andrew J. Black, John Maclean

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Neural Networks Can Be FLOP-Efficient Integrators of 1d Oscillatory Integrands Anshuman Sinha, Spencer H Bryngelson

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Neural Task Synthesis for Visual Programming Victor-Alexandru Pădurean, Georgios Tzannetos, Adish Singla

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New Evaluation Metrics Capture Quality Degradation Due to LLM Watermarking Karanpartap Singh, James Zou

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New Guarantees for Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs Maria Florina Balcan, Hedyeh Beyhaghi

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No Identity, No Problem: Motion Through Detection for People Tracking Martin Engilberge, Friedrich Wilke Grosche, Pascal Fua

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Node-Specific Space Selection via Localized Geometric Hyperbolicity in Graph Neural Networks See Hian Lee, Feng Ji, Wee Peng Tay

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Noise Stability Optimization for Finding Flat Minima: A Hessian-Based Regularization Approach Hongyang R. Zhang, Dongyue Li, Haotian Ju

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Non-Backtracking Graph Neural Networks Seonghyun Park, Narae Ryu, Gahee Kim, Dongyeop Woo, Se-Young Yun, Sungsoo Ahn

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Non-Stationary Dueling Bandits Under a Weighted Borda Criterion Joe Suk, Arpit Agarwal

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Non-Uniform Smoothness for Gradient Descent Albert S. Berahas, Lindon Roberts, Fred Roosta

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Nonlinear Behaviour of Critical Points for a Simple Neural Network Gerrit Welper

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NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning Zhongying Deng, Rihuan Ke, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero

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Normed Spaces for Graph Embedding Diaaeldin Taha, Wei Zhao, J. Maxwell Riestenberg, Michael Strube

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Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation Aahlad Manas Puli, Nitish Joshi, Yoav Wald, He He, Rajesh Ranganath

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NuTime: Numerically Multi-Scaled Embedding for Large- Scale Time-Series Pretraining Chenguo Lin, Xumeng Wen, Wei Cao, Congrui Huang, Jiang Bian, Stephen Lin, Zhirong Wu

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Object-Centric Relational Representations for Image Generation Luca Butera, Andrea Cini, Alberto Ferrante, Cesare Alippi

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Offline Deep Reinforcement Learning for Visual Distractions via Domain Adversarial Training Jen-Yen Chang, Thomas Westfechtel, Takayuki Osa, Tatsuya Harada

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Offline Reinforcement Learning via Tsallis Regularization Lingwei Zhu, Matthew Kyle Schlegel, Han Wang, Martha White

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OmniPred: Language Models as Universal Regressors Xingyou Song, Oscar Li, Chansoo Lee, Bangding Yang, Daiyi Peng, Sagi Perel, Yutian Chen

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On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models Juliette Marrie, Michael Arbel, Julien Mairal, Diane Larlus

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On Intriguing Layer-Wise Properties of Robust Overfitting in Adversarial Training Duke Nguyen, Chaojian Yu, Vinoth Nandakumar, Young Choon Lee, Tongliang Liu

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On Safety in Safe Bayesian Optimization Christian Fiedler, Johanna Menn, Lukas Kreisköther, Sebastian Trimpe

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On the Adversarial Robustness of Camera-Based 3D Object Detection Shaoyuan Xie, Zichao Li, Zeyu Wang, Cihang Xie

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On the Choice of Learning Rate for Local SGD Lukas Balles, Prabhu Teja S, Cedric Archambeau

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On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization Dongruo Zhou, Jinghui Chen, Yuan Cao, Ziyan Yang, Quanquan Gu

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On the Data Heterogeneity in Adaptive Federated Learning Yujia Wang, Jinghui Chen

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On the Dual Problem of Convexified Convolutional Neural Networks Site Bai, Chuyang Ke, Jean Honorio

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On the Equivalence of Graph Convolution and Mixup Xiaotian Han, Hanqing Zeng, Yu Chen, Shaoliang Nie, Jingzhou Liu, Kanika Narang, Zahra Shakeri, Karthik Abinav Sankararaman, Song Jiang, Madian Khabsa, Qifan Wang, Xia Hu

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On the Importance of Uncertainty in Decision-Making with Large Language Models Nicolò Felicioni, Lucas Maystre, Sina Ghiassian, Kamil Ciosek

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On the Inherent Privacy Properties of Discrete Denoising Diffusion Models Rongzhe Wei, Eleonora Kreacic, Haoyu Peter Wang, Haoteng Yin, Eli Chien, Vamsi K. Potluru, Pan Li

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On the Interdependence Between Data Selection and Architecture Optimization in Deep Active Learning Pradeep Bajracharya, Rui Li, Linwei Wang

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On the Numerical Reliability of Nonsmooth Autodiff: A MaxPool Case Study Ryan Boustany

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On the Optimization and Generalization of Multi-Head Attention Puneesh Deora, Rouzbeh Ghaderi, Hossein Taheri, Christos Thrampoulidis

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On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning Paul Scemama, Ariel Kapusta

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On the Reproducibility of: "Learning Perturbations to Explain Time Series Predictions" Wouter Bant, Ádám Divák, Jasper Eppink, Floris Six Dijkstra

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On the Robustness of Neural Collapse and the Neural Collapse of Robustness Jingtong Su, Ya Shi Zhang, Nikolaos Tsilivis, Julia Kempe

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On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization Ling Liang, Haizhao Yang

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On the Theoretical Limit of Gradient Descent for Simple Recurrent Neural Networks with Finite Precision Volodimir Mitarchuk, Rémi Emonet, Remi Eyraud, Amaury Habrard

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On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang

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One by One, Continual Coordinating with Humans via Hyper-Teammate Identification Cong Guan, Feng Chen, Ke Xue, Chunpeng Fan, Lichao Zhang, Ziqian Zhang, Pengyao Zhao, Zongzhang Zhang, Chao Qian, Lei Yuan, Yang Yu

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Online Continual Learning via Logit Adjusted SoftMax Zhehao Huang, Tao Li, Chenhe Yuan, Yingwen Wu, Xiaolin Huang

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Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits Yue Kang, Cho-Jui Hsieh, Thomas Lee

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Online Reference Tracking for Linear Systems with Unknown Dynamics and Unknown Disturbances Nariman Niknejad, Farnaz Adib Yaghmaie, Hamidreza Modares

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Online Tensor Max-Norm Regularization via Stochastic Optimization Tong Wu

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Oops, I Sampled It Again: Reinterpreting Confidence Intervals in Few-Shot Learning Raphael Lafargue, Luke A Smith, Franck Vermet, Matthias Löwe, Ian Reid, Jack Valmadre, Vincent Gripon

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Optical Transformers Maxwell Anderson, Shi-Yuan Ma, Tianyu Wang, Logan Wright, Peter McMahon

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Optimal Inference in Contextual Stochastic Block Models O Duranthon, Lenka Zdeborova

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Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees James Queeney, Erhan Can Ozcan, Ioannis Paschalidis, Christos Cassandras

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Optimization with Access to Auxiliary Information El Mahdi Chayti, Sai Praneeth Karimireddy

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Optimized Tradeoffs for Private Prediction with Majority Ensembling Shuli Jiang, Qiuyi Zhang, Gauri Joshi

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Orthogonal Random Features: Explicit Forms and Sharp Inequalities Nizar Demni, Hachem Kadri

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Out-of-Distribution Optimality of Invariant Risk Minimization Shoji Toyota, Kenji Fukumizu

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Overcoming Order in Autoregressive Graph Generation for Molecule Generation Edo Cohen-Karlik, Eyal Rozenberg, Daniel Freedman

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Overcoming the Stability Gap in Continual Learning Md Yousuf Harun, Christopher Kanan

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PaDPaF: Partial Disentanglement with Partially-Federated GANs Abdulla Jasem Almansoori, Samuel Horváth, Martin Takáč

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Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey Zeyu Han, Chao Gao, Jinyang Liu, Jeff Zhang, Sai Qian Zhang

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PASS: Pruning Attention Heads with Almost-Sure Sparsity Targets Dujian Ding, Ganesh Jawahar, Laks V. S. Lakshmanan

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Path Development Network with Finite-Dimensional Lie Group Hang Lou, Siran Li, Hao Ni

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Pathologies of Predictive Diversity in Deep Ensembles Taiga Abe, E. Kelly Buchanan, Geoff Pleiss, John Patrick Cunningham

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PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans Giang Nguyen, Valerie Chen, Mohammad Reza Taesiri, Anh Totti Nguyen

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Perception Stitching: Zero-Shot Perception Encoder Transfer for Visuomotor Robot Policies Pingcheng Jian, Easop Lee, Zachary I. Bell, Michael M. Zavlanos, Boyuan Chen

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Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in Games Chiu-Chou Lin, Wei-Chen Chiu, I-Chen Wu

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PerSEval: Assessing Personalization in Text Summarizers Sourish Dasgupta, Ankush Chander, Tanmoy Chakraborty, Parth Borad, Isha Motiyani

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Persistent Local Homology in Graph Learning Minghua Wang, Yan Hu, Ziyun Huang, Di Wang, Jinhui Xu

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Persona-Aware Generative Model for Code-Mixed Language Ayan Sengupta, Md Shad Akhtar, Tanmoy Chakraborty

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Personalised Federated Learning on Heterogeneous Feature Spaces Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz, Liva Ralaivola

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Personalized Algorithmic Recourse with Preference Elicitation Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, Andrea Passerini

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Personalized Federated Learning with Spurious Features: An Adversarial Approach Xiaoyang Wang, Han Zhao, Klara Nahrstedt, Sanmi Koyejo

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Physical Reasoning and Object Planning for Household Embodied Agents Ayush Agrawal, Raghav Prabhakar, Anirudh Goyal, Dianbo Liu

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Physics Informed Distillation for Diffusion Models Joshua Tian Jin Tee, Kang Zhang, Hee Suk Yoon, Dhananjaya Nagaraja Gowda, Chanwoo Kim, Chang D. Yoo

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PID Control-Based Self-Healing to Improve the Robustness of Large Language Models Zhuotong Chen, Zihu Wang, Yifan Yang, Qianxiao Li, Zheng Zhang

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Piecewise-Stationary Dueling Bandits Patrick Kolpaczki, Eyke Hüllermeier, Viktor Bengs

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PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling Yuan Liu, Songyang Zhang, Jiacheng Chen, Kai Chen, Dahua Lin

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Planning with Consistency Models for Model-Based Offline Reinforcement Learning Guanquan Wang, Takuya Hiraoka, Yoshimasa Tsuruoka

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Plug, Play, and Generalize: Length Extrapolation with Pointer-Augmented Neural Memory Hung Le, Dung Nguyen, Kien Do, Svetha Venkatesh, Truyen Tran

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PLUM: Improving Inference Efficiency by Leveraging Repetition-Sparsity Trade-Off Sachit Kuhar, Yash Jain, Alexey Tumanov

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PNeRV: A Polynomial Neural Representation for Videos Sonam Gupta, Snehal Singh Tomar, Grigorios Chrysos, Sukhendu Das, Rajagopalan N Ambasamduram

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Policy Gradient with Kernel Quadrature Satoshi Hayakawa, Tetsuro Morimura

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PopulAtion Parameter Averaging (PAPA) Alexia Jolicoeur-Martineau, Emy Gervais, Kilian Fatras, Yan Zhang, Simon Lacoste-Julien

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Population Priors for Matrix Factorization Sohrab Salehi, Achille Nazaret, Sohrab P Shah, David Blei

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Positional Encoding Helps Recurrent Neural Networks Handle a Large Vocabulary Takashi Morita

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Practical Synthesis of Mixed-Tailed Data with Normalizing Flows Saba Amiri, Eric Nalisnick, Adam Belloum, Sander Klous, Leon Gommans

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PRD: Peer Rank and Discussion Improve Large Language Model Based Evaluations Ruosen Li, Teerth Patel, Xinya Du

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Pre-Trained Hypergraph Convolutional Neural Networks with Self-Supervised Learning Yihe Deng, Ruochi Zhang, Pan Xu, Jian Ma, Quanquan Gu

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Preconditioned Neural Posterior Estimation for Likelihood-Free Inference Xiaoyu Wang, Ryan P. Kelly, David J Warne, Christopher Drovandi

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Predicting the Encoding Error of SIRENs Jeremy Vonderfecht, Feng Liu

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Predictive Pipelined Decoding: A Compute-Latency Trade-Off for Exact LLM Decoding Seongjun Yang, Gibbeum Lee, Jaewoong Cho, Dimitris Papailiopoulos, Kangwook Lee

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Pretrained Deep Models Outperform GBDTs in Learning-to-Rank Under Label Scarcity Charlie Hou, Kiran Koshy Thekumparampil, Michael Shavlovsky, Giulia Fanti, Yesh Dattatreya, Sujay Sanghavi

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Pretraining a Neural Operator in Lower Dimensions AmirPouya Hemmasian, Amir Barati Farimani

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Prioritized Federated Learning: Leveraging Non-Priority Clients for Targeted Model Improvement Aditya Narayan Ravi, Ilan Shomorony

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Prismer: A Vision-Language Model with Multi-Task Experts Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, Anima Anandkumar

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Privacy Preserving Reinforcement Learning for Population Processes Samuel Yang-Zhao, Kee Siong Ng

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Privacy-Preserving Split Learning with Vision Transformers Using Patch-Wise Random and Noisy CutMix Seungeun Oh, Sihun Baek, Jihong Park, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim

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PriViT: Vision Transformers for Private Inference Naren Dhyani, Jianqiao Cambridge Mo, Patrick Yubeaton, Minsu Cho, Ameya Joshi, Siddharth Garg, Brandon Reagen, Chinmay Hegde

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Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks Philipp Pilar, Niklas Wahlström

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ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations Sravanti Addepalli, Priyam Dey, Venkatesh Babu Radhakrishnan

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Promoting Exploration in Memory-Augmented Adam Using Critical Momenta Pranshu Malviya, Goncalo Mordido, Aristide Baratin, Reza Babanezhad Harikandeh, Jerry Huang, Simon Lacoste-Julien, Razvan Pascanu, Sarath Chandar

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Prototypical Self-Explainable Models Without Re-Training Srishti Gautam, Ahcene Boubekki, Marina MC Höhne, Michael Kampffmeyer

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Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns Chuyang Ke, Jean Honorio

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Provable Membership Inference Privacy Zachary Izzo, Jinsung Yoon, Sercan O Arik, James Zou

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Proximal Mean Field Learning in Shallow Neural Networks Alexis Teter, Iman Nodozi, Abhishek Halder

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Pseudo-Differential Neural Operator: Generalize Fourier Neural Operator for Learning Solution Operators of Partial Differential Equations Jin Young Shin, Jae Yong Lee, Hyung Ju Hwang

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Pull-Back Geometry of Persistent Homology Encodings Shuang Liang, Renata Turkes, Jiayi Li, Nina Otter, Guido Montufar

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Pushing the Limits of Gradient Descent for Efficient Learning on Large Images Deepak Gupta, Gowreesh Mago, Arnav Chavan, Dilip Prasad, Rajat Mani Thomas

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Q-Learning for Stochastic Control Under General Information Structures and Non-Markovian Environments Ali Devran Kara, Serdar Yuksel

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QDC: Quantum Diffusion Convolution Kernels on Graphs Thomas Markovich

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Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precision Xijie Huang, Zhiqiang Shen, Pingcheng Dong, Kwang-Ting Cheng

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Re-Thinking Inverse Graphics with Large Language Models Peter Kulits, Haiwen Feng, Weiyang Liu, Victoria Fernandez Abrevaya, Michael J. Black

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Read Between the Layers: Leveraging Multi-Layer Representations for Rehearsal-Free Continual Learning with Pre-Trained Models Kyra Ahrens, Hans Hergen Lehmann, Jae Hee Lee, Stefan Wermter

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Recent Link Classification on Temporal Graphs Using Graph Profiler Muberra Ozmen, Thomas Markovich

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Reconciling Kaplan and Chinchilla Scaling Laws Tim Pearce, Jinyeop Song

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Recovering Exact Support in Federated Lasso Without Optimization Adarsh Barik, Jean Honorio

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Recurrent Inertial Graph-Based Estimator (RING): A Single Pluripotent Inertial Motion Tracking Solution Simon Bachhuber, Ive Weygers, Dustin Lehmann, Mischa Dombrowski, Thomas Seel

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RedMotion: Motion Prediction via Redundancy Reduction Royden Wagner, Omer Sahin Tas, Marvin Klemp, Carlos Fernandez, Christoph Stiller

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Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks Alfredo Reichlin, Gustaf Tegnér, Miguel Vasco, Hang Yin, Mårten Björkman, Danica Kragic

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Regret Bounds for Noise-Free Cascaded Kernelized Bandits Zihan Li, Jonathan Scarlett

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Regularized Proportional Fairness Mechanism for Resource Allocation Without Money Sihan Zeng, Sujay Bhatt, Alec Koppel, Sumitra Ganesh

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Reinforcement Learning for Node Selection in Branch-and-Bound Alexander Julian Mattick, Christopher Mutschler

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Repositioning the Subject Within Image Yikai Wang, Chenjie Cao, Ke Fan, Qiaole Dong, Yifan Li, Xiangyang Xue, Yanwei Fu

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Representation Learning Dynamics of Self-Supervised Models Pascal Esser, Satyaki Mukherjee, Debarghya Ghoshdastidar

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Representation Norm Amplification for Out-of-Distribution Detection in Long-Tail Learning Dong Geun Shin, Hye Won Chung

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Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research. Tobias Hille, Maximilian Stubbemann, Tom Hanika

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Reproducibility Study of "Explaining RL Decisions with Trajectories" Clio Feng, Colin Bot, Bart den Boef, Bart Aaldering

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Reproducibility Study of "ITI-GEN: Inclusive Text-to-Image Generation" Daniel Gallo Fernández, Răzvan-Andrei Matișan, Alejandro Monroy Muñoz, Janusz Partyka

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Reproducibility Study of "Languange-Image COnsistency" Konrad Szewczyk, Patrik Bartak, Mikhail Vlasenko, Fanmin Shi

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Reproducibility Study of "Learning Perturbations to Explain Time Series Predictions" Jiapeng Fan, Luke Cadigan, Paulius Skaisgiris, Sebastian Uriel Arias

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Reproducibility Study of "Robust Fair Clustering: A Novel Fairness Attack and Defense Framework" Lucas Ponticelli, Vincent Loos, Eren Kocadag, Kacper Bartosik

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Reproducibility Study of "Robust Fair Clustering: A Novel Fairness Attack and Defense Framework" Iason Skylitsis, Zheng Feng, Idries Nasim, Camille Niessink

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Reproducibility Study of “LICO: Explainable Models with Language-Image Consistency" Luan Fletcher, Robert van der Klis, Martin Sedláček, Stefan Vasilev, Christos Athanasiadis

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Reproducibility Study of FairAC Gijs de Jong, Macha J. Meijer, Derck W. E. Prinzhorn, Harold Ruiter

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Reproducibility Study of Learning Fair Graph Representations via Automated Data Augmentations Thijmen Nijdam, Juell Sprott, Taiki Papandreou-Lazos, Jurgen de Heus

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Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers Fatemeh Nourilenjan Nokabadi, Jean-Francois Lalonde, Christian Gagné

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Reproducibility Study: Equal Improvability: A New Fairness Notion Considering the Long-Term Impact Berkay Chakar, Amina Izbassar, Mina Janićijević, Jakub Tomaszewski

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Restricted Random Pruning at Initialization for High Compression Range Hikari Otsuka, Yasuyuki Okoshi, Ángel López García-Arias, Kazushi Kawamura, Thiem Van Chu, Daichi Fujiki, Masato Motomura

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Rethinking Teacher-Student Curriculum Learning Through the Cooperative Mechanics of Experience Manfred Diaz, Liam Paull, Andrea Tacchetti

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Revealing an Overlooked Challenge in Class-Incremental Graph Learning Daiqing Qi, Handong Zhao, Xiaowei Jia, Sheng Li

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Revisiting Active Learning in the Era of Vision Foundation Models Sanket Rajan Gupte, Josiah Aklilu, Jeffrey J Nirschl, Serena Yeung-Levy

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Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection Sukanya Patra, Souhaib Ben Taieb

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Revisiting Discrete Soft Actor-Critic Haibin Zhou, Tong Wei, Zichuan Lin, Junyou Li, Junliang Xing, Yuanchun Shi, Li Shen, Chao Yu, Deheng Ye

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Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models Sumeet Singh, Stephen Tu, Vikas Sindhwani

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Revisiting Feature Prediction for Learning Visual Representations from Video Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann LeCun, Mido Assran, Nicolas Ballas

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Revisiting Generalized P-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and as Non-Linear Diffusion Dai Shi, Zhiqi Shao, Yi Guo, Qibin Zhao, Junbin Gao

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Revisiting Non-Separable Binary Classification and Its Applications in Anomaly Detection Matthew Lau, Ismaila Seck, Athanasios P Meliopoulos, Wenke Lee, Eugene Ndiaye

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Revisiting Random Weight Perturbation for Efficiently Improving Generalization Tao Li, Qinghua Tao, Weihao Yan, Yingwen Wu, Zehao Lei, Kun Fang, Mingzhen He, Xiaolin Huang

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Revisiting Stochastic Submodular Maximization with Cardinality Constraint: A Bandit Perspective Pratik Jawanpuria, Bamdev Mishra, Karthik S. Gurumoorthy

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Reward Guided Latent Consistency Distillation Jiachen Li, Weixi Feng, Wenhu Chen, William Yang Wang

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Reward Poisoning on Federated Reinforcement Learning Evelyn Ma, S. Rasoul Etesami, Praneet Rathi

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Risk Bounds for Mixture Density Estimation on Compact Domains via the H-Lifted Kullback–Leibler Divergence Mark Chiu Chong, Hien Duy Nguyen, TrungTin Nguyen

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Risk-Controlling Model Selection via Guided Bayesian Optimization Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi Jaakkola

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RLHF Workflow: From Reward Modeling to Online RLHF Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang

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RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Manon Devin, Alex X. Lee, Maria Bauza Villalonga, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Fernandes Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott Reed, Sergio Gómez Colmenarejo, Jonathan Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, Jose Enrique Chen, Yusuf Aytar, David Barker, Joy Ortiz, Martin Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess

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Robust and Efficient Quantization-Aware Training via Coreset Selection Xijie Huang, Zechun Liu, Shih-Yang Liu, Kwang-Ting Cheng

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Robust Distortion-Free Watermarks for Language Models Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto, Percy Liang

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Robust Feature Inference: A Test-Time Defense Strategy Using Spectral Projections Anurag Singh, Mahalakshmi Sabanayagam, Krikamol Muandet, Debarghya Ghoshdastidar

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Robust Guided Diffusion for Offline Black-Box Optimization Can Chen, Christopher Beckham, Zixuan Liu, Xue Liu, Christopher Pal

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Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic Matteo Sordello, Niccolo Dalmasso, Hangfeng He, Weijie J Su

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Robust Stochastic Optimization via Gradient Quantile Clipping Ibrahim Merad, Stéphane Gaïffas

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Rotate the ReLU to Sparsify Deep Networks Implicitly Nancy Nayak, Sheetal Kalyani

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Routers in Vision Mixture of Experts: An Empirical Study Tianlin Liu, Mathieu Blondel, Carlos Riquelme Ruiz, Joan Puigcerver

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SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP Jie Chen, Mingyuan Bai, Shouzhen Chen, Junbin Gao, Junping Zhang, Jian Pu

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SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer Renan A. Rojas-Gomez, Karan Singhal, Ali Etemad, Alex Bijamov, Warren Richard Morningstar, Philip Andrew Mansfield

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Scalable Hierarchical Self-Attention with Learnable Hierarchy for Long-Range Interactions Thuan Nguyen Anh Trang, Khang Nhat Ngo, Hugo Sonnery, Thieu Vo, Siamak Ravanbakhsh, Truong Son Hy

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Scale Equalization for Multi-Level Feature Fusion Bum Jun Kim, Sang Woo Kim

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Scaling (Down) CLIP: A Comprehensive Analysis of Data,Architecture, and Training Strategies Zichao Li, Cihang Xie, Ekin Dogus Cubuk

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Scaling Laws for Imitation Learning in Single-Agent Games Jens Tuyls, Dhruv Madeka, Kari Torkkola, Dean Foster, Karthik R Narasimhan, Sham M. Kakade

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Scaling up Bayesian Neural Networks with Neural Networks Zahra Moslemi, Yang Meng, Shiwei Lan, Babak Shahbaba

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Scaling Vision-and-Language Navigation with Offline RL Valay Bundele, Mahesh Bhupati, Biplab Banerjee, Aditya Grover

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Score-Based Explainability for Graph Representations Ehsan Hajiramezanali, Sepideh Maleki, Max W Shen, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia

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Score-Based Multimodal Autoencoder Daniel Wesego, Pedram Rooshenas

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SEAL: Simultaneous Label Hierarchy Exploration and Learning Zhiquan Tan, Zihao Wang, Yifan Zhang

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Selective Classification Under Distribution Shifts Hengyue Liang, Le Peng, Ju Sun

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Selective Pre-Training for Private Fine-Tuning Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang

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Self-Improvement for Neural Combinatorial Optimization: Sample Without Replacement, but Improvement Jonathan Pirnay, Dominik G. Grimm

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Self-Supervised Color Generalization in Reinforcement Learning Matthias Weissenbacher, Evangelos Routis, Yoshinobu Kawahara

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Self-Supervised Visual Representation Learning for Medical Image Analysis: A Comprehensive Survey Siladittya Manna, Saumik Bhattacharya, Umapada Pal

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SelfXit: An Unsupervised Early Exit Mechanism for Deep Neural Networks Hossein KhademSohi, Mohammadamin Abedi, Yani Ioannou, Steve Drew, Pooyan Jamshidi, Hadi Hemmati

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Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination Methods Mohammad Alkhalefi, Georgios Leontidis, Mingjun Zhong

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Semi-Supervised Semantic Segmentation via Marginal Contextual Information Moshe Kimhi, Shai Kimhi, Evgenii Zheltonozhskii, Or Litany, Chaim Baskin

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Sensitivity-Aware Amortized Bayesian Inference Lasse Elsemüller, Hans Olischläger, Marvin Schmitt, Paul-Christian Bürkner, Ullrich Koethe, Stefan T. Radev

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Separability Analysis for Causal Discovery in Mixture of DAGs Burak Varici, Dmitriy Katz, Dennis Wei, Prasanna Sattigeri, Ali Tajer

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Separable Operator Networks Xinling Yu, Sean Hooten, Ziyue Liu, Yequan Zhao, Marco Fiorentino, Thomas Van Vaerenbergh, Zheng Zhang

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SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time Series Futoon M. Abushaqra, Hao Xue, Yongli Ren, Flora D. Salim

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Sequential Best-Arm Identification with Application to P300 Speller Xin Zhou, Botao Hao, Tor Lattimore, Jian Kang, Lexin Li

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Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks Elre Talea Oldewage, Ross M Clarke, José Miguel Hernández-Lobato

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Set Features for Anomaly Detection Niv Cohen, Issar Tzachor, Yedid Hoshen

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Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics Haoqin Tu, Bingchen Zhao, Chen Wei, Cihang Xie

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Simple and Scalable Strategies to Continually Pre-Train Large Language Models Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats Leon Richter, Quentin Gregory Anthony, Eugene Belilovsky, Timothée Lesort, Irina Rish

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Simple Drop-in LoRA Conditioning on Attention Layers Will Improve Your Diffusion Model Joo Young Choi, Jaesung R. Park, Inkyu Park, Jaewoong Cho, Albert No, Ernest K. Ryu

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Simple Imputation Rules for Prediction with Missing Data: Theoretical Guarantees vs. Empirical Performance Dimitris Bertsimas, Arthur Delarue, Jean Pauphilet

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Simple Steps to Success: A Method for Step-Based Counterfactual Explanations Jenny Hamer, Nicholas Perello, Jason Valladares, Vignesh Viswanathan, Yair Zick

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Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learning Eslam Abdelaleem, Ahmed Roman, K. Michael Martini, Ilya Nemenman

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Single Image Test-Time Adaptation for Segmentation Klara Janouskova, Tamir Shor, Chaim Baskin, Jiri Matas

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Single-Shot Plug-and-Play Methods for Inverse Problems Yanqi Cheng, Lipei Zhang, Zhenda Shen, Shujun Wang, Lequan Yu, Raymond H. Chan, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero

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Size Lowerbounds for Deep Operator Networks Anirbit Mukherjee, Amartya Roy

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Sketch and Shift: A Robust Decoder for Compressive Clustering Ayoub Belhadji, Rémi Gribonval

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Smoothed Robustness Analysis: Bridging Worst- and Average-Case Robustness Analyses via Smoothed Analysis Thomas Rodrigues Crespo, Jun-nosuke Teramae

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Soft Merging of Experts with Adaptive Routing Mohammed Muqeeth, Haokun Liu, Colin Raffel

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Solving Inverse Problems with Model Mismatch Using Untrained Neural Networks Within Model-Based Architectures Peimeng Guan, Naveed Iqbal, Mark A. Davenport, Mudassir Masood

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Solving Robust MDPs Through No-Regret Dynamics Etash Kumar Guha

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Solving the Tree Containment Problem Using Graph Neural Networks Arkadiy Dushatskiy, Esther Julien, Leen Stougie, Leo van Iersel

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Sparse Contextual CDF Regression Kamyar Azizzadenesheli, William Lu, Anuran Makur, Qian Zhang

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Sparse Modal Regression with Mode-Invariant Skew Noise Kazuki Koyama, Takayuki Kawashima, Hironori Fujisawa

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Sparsifying Bayesian Neural Networks with Latent Binary Variables and Normalizing Flows Lars Skaaret-Lund, Geir Storvik, Aliaksandr Hubin

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Spectral Self-Supervised Feature Selection Daniel Segal, Ofir Lindenbaum, Ariel Jaffe

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Spike Accumulation Forwarding for Effective Training of Spiking Neural Networks Ryuji Saiin, Tomoya Shirakawa, Sota Yoshihara, Yoshihide Sawada, Hiroyuki Kusumoto

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SpikeGPT: Generative Pre-Trained Language Model with Spiking Neural Networks Rui-Jie Zhu, Qihang Zhao, Guoqi Li, Jason Eshraghian

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SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery Ajinkya K Mulay, Xiaojun Lin

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SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL Ruoxi Sun, Sercan O Arik, Alexandre Muzio, Lesly Miculicich, Satya Kesav Gundabathula, Pengcheng Yin, Hanjun Dai, Hootan Nakhost, Rajarishi Sinha, Zifeng Wang, Tomas Pfister

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Stability and Generalization in Free Adversarial Training Xiwei Cheng, Kexin Fu, Farzan Farnia

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Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Olukorede Fakorede, Modeste Atsague, Jin Tian

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State-Wise Constrained Policy Optimization Weiye Zhao, Rui Chen, Yifan Sun, Feihan Li, Tianhao Wei, Changliu Liu

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Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models Qiujiang Jin, Tongzheng Ren, Nhat Ho, Aryan Mokhtari

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Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures Bruno Régaldo-Saint Blancard, Michael Eickenberg

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Statistical Mechanics of Min-Max Problems Yuma Ichikawa, Koji Hukushima

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Stealthy Backdoor Attack via Confidence-Driven Sampling Pengfei He, Yue Xing, Han Xu, Jie Ren, Yingqian Cui, Shenglai Zeng, Jiliang Tang, Makoto Yamada, Mohammad Sabokrou

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Stochastic Bandits for Egalitarian Assignment Eugene Lim, Vincent Y. F. Tan, Harold Soh

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Stochastic Direct Search Methods for Blind Resource Allocation Juliette Achddou, Olivier Cappé, Aurélien Garivier

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Stochastic Re-Weighted Gradient Descent via Distributionally Robust Optimization Ramnath Kumar, Kushal Alpesh Majmundar, Dheeraj Mysore Nagaraj, Arun Suggala

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Strategies for Pretraining Neural Operators Anthony Zhou, Cooper Lorsung, AmirPouya Hemmasian, Amir Barati Farimani

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Strengthening Interpretability: An Investigative Study of Integrated Gradient Methods Shree Singhi, Anupriya Kumari

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Structural Pruning of Pre-Trained Language Models via Neural Architecture Search Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cedric Archambeau

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Structure-Preserving Network Compression via Low-Rank Induced Training Through Linear Layers Composition Ismail Alkhouri, Xitong Zhang, Rongrong Wang

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Supervised Domain Adaptation Based on Marginal and Conditional Distributions Alignment Ori Katz, Ronen Talmon, Uri Shaham

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Support-Set Context Matters for Bongard Problems Nikhil Raghuraman, Adam W Harley, Leonidas Guibas

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SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation Qi Yan, Zhengyang Liang, Yang Song, Renjie Liao, Lele Wang

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Switching Latent Bandits Alessio Russo, Alberto Maria Metelli, Marcello Restelli

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Synaptic Interaction Penalty: Appropriate Penalty Term for Energy-Efficient Spiking Neural Networks Kazuma Suetake, Takuya Ushimaru, Ryuji Saiin, Yoshihide Sawada

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Synthesizing Libraries of Programs with Auxiliary Functions Habibur Rahman, Thirupathi Reddy Emireddy, Kenneth Tjhia, Elham Parhizkar, Levi Lelis

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Synthetic Data Shuffling Accelerates the Convergence of Federated Learning Under Data Heterogeneity Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U Stich

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Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference Max Lam, Michael Mitzenmacher, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks

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TacoGFN: Target-Conditioned GFlowNet for Structure-Based Drug Design Tony Shen, Seonghwan Seo, Grayson Lee, Mohit Pandey, Jason R Smith, Artem Cherkasov, Woo Youn Kim, Martin Ester

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TAP: The Attention Patch for Cross-Modal Knowledge Transfer from Unlabeled Modality Yinsong Wang, Shahin Shahrampour

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Targeted Active Learning for Bayesian Decision-Making Louis Filstroff, Iiris Sundin, Petrus Mikkola, Aleksei Tiulpin, Juuso Kylmäoja, Samuel Kaski

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Task-Relevant Feature Selection with Prediction Focused Mixture Models Abhishek Sharma, Catherine Zeng, Sanjana Narayanan, Sonali Parbhoo, Roy H. Perlis, Finale Doshi-Velez

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Teacher-Guided Graph Contrastive Learning Jay Nandy, Arnab Kumar Mondal, Manohar Kaul, Prathosh Ap

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TeaMs-RL: Teaching LLMs to Generate Better Instruction Datasets via Reinforcement Learning Shangding Gu, Alois Knoll, Ming Jin

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Temporal Difference Learning with Compressed Updates: Error-Feedback Meets Reinforcement Learning Aritra Mitra, George J. Pappas, Hamed Hassani

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Temporally Rich Deep Learning Models for Magnetoencephalography Tim Chard, Mark Dras, Paul Sowman, Steve Cassidy, Jia Wu

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TensorVAE: A Simple and Efficient Generative Model for Conditional Molecular Conformation Generation Hongyang Yu, Hongjiang Yu

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Text Descriptions Are Compressive and Invariant Representations for Visual Learning Zhili Feng, Anna Bair, J Zico Kolter

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The Cold Posterior Effect Indicates Underfitting, and Cold Posteriors Represent a Fully Bayesian Method to Mitigate It Yijie Zhang, Yi-Shan Wu, Luis A. Ortega, Andres R Masegosa

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The Cross-Entropy of Piecewise Linear Probability Density Functions Tom S. F. Haines

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The Disagreement Problem in Explainable Machine Learning: A Practitioner’s Perspective Satyapriya Krishna, Tessa Han, Alex Gu, Steven Wu, Shahin Jabbari, Himabindu Lakkaraju

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The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning Justin Singh Kang, Ramtin Pedarsani, Kannan Ramchandran

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The Harmonic Indel Distance Bob Pepin

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The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation Nicolas Guerin, Emmanuel Chemla, Shane Steinert-Threlkeld

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The Interplay of Uncertainty Modeling and Deep Active Learning: An Empirical Analysis in Image Classification Denis Huseljic, Marek Herde, Yannick Nagel, Lukas Rauch, Paulius Strimaitis, Bernhard Sick

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The Kernel Perspective on Dynamic Mode Decomposition Efrain Gonzalez, Moad Abudia, Michael Jury, Rushikesh Kamalapurkar, Joel A Rosenfeld

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The Klarna Product Page Dataset: Web Element Nomination with Graph Neural Networks and Large Language Models Alexandra Hotti, Riccardo Sven Risuleo, Stefan Magureanu, Aref Moradi, Jens Lagergren

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The Missing U for Efficient Diffusion Models Sergio Calvo Ordoñez, Chun-Wun Cheng, Jiahao Huang, Lipei Zhang, Guang Yang, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero

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The Real Tropical Geometry of Neural Networks for Binary Classification Marie-Charlotte Brandenburg, Georg Loho, Guido Montufar

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The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources Shayne Longpre, Stella Biderman, Alon Albalak, Hailey Schoelkopf, Daniel McDuff, Sayash Kapoor, Kevin Klyman, Kyle Lo, Gabriel Ilharco, Nay San, Maribeth Rauh, Aviya Skowron, Bertie Vidgen, Laura Weidinger, Arvind Narayanan, Victor Sanh, David Ifeoluwa Adelani, Percy Liang, Rishi Bommasani, Peter Henderson, Sasha Luccioni, Yacine Jernite, Luca Soldaini

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The Slingshot Effect: A Late-Stage Optimization Anomaly in Adaptive Gradient Methods Vimal Thilak, Etai Littwin, Shuangfei Zhai, Omid Saremi, Roni Paiss, Joshua M. Susskind

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The Survival Bandit Problem Charles Riou, Junya Honda, Masashi Sugiyama

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The Trifecta: Three Simple Techniques for Training Deeper Forward-Forward Networks Thomas Dooms, Ing Jyh Tsang, Jose Oramas

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The Unreasonable Effectiveness of Gaussian Score Approximation for Diffusion Models and Its Applications Binxu Wang, John Vastola

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Threshold Moving for Online Class Imbalance Learning with Dynamic Evolutionary Cost Vector Peijia Qin, Shuxian Li, Xiaoqun Liu, Zubin Zheng, Siang Yew Chong

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TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks Dongfu Jiang, Yishan Li, Ge Zhang, Wenhao Huang, Bill Yuchen Lin, Wenhu Chen

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Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations Etienne Le Naour, Louis Serrano, Léon Migus, Yuan Yin, Ghislain Agoua, Nicolas Baskiotis, Patrick Gallinari, Vincent Guigue

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To Transfer or Not to Transfer: Suppressing Concepts from Source Representations Vijay Sadashivaiah, Keerthiram Murugesan, Ronny Luss, Pin-Yu Chen, Chris Sims, James Hendler, Amit Dhurandhar

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Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization Mahdi Biparva, Raika Karimi, Faezeh Faez, Yingxue Zhang

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TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis Sabera J Talukder, Yisong Yue, Georgia Gkioxari

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Toward a Complete Criterion for Value of Information in Insoluble Decision Problems Ryan Carey, Sanghack Lee, Robin J. Evans

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Towards Backwards-Compatible Data with Confounded Domain Adaptation Calvin McCarter

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Towards Empirical Interpretation of Internal Circuits and Properties in Grokked Transformers on Modular Polynomials Hiroki Furuta, Gouki Minegishi, Yusuke Iwasawa, Yutaka Matsuo

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Towards Fully Covariant Machine Learning Soledad Villar, David W Hogg, Weichi Yao, George A Kevrekidis, Bernhard Schölkopf

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Towards Generalizing Deep-Audio Fake Detection Networks Konstantin Gasenzer, Moritz Wolter

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Towards Minimal Targeted Updates of Language Models with Targeted Negative Training Lily H Zhang, Rajesh Ranganath, Arya Tafvizi

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Towards Provable Log Density Policy Gradient Pulkit Katdare, Anant A Joshi, Katherine Rose Driggs-Campbell

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Towards Size-Independent Generalization Bounds for Deep Operator Nets Pulkit Gopalani, Sayar Karmakar, Dibyakanti Kumar, Anirbit Mukherjee

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Towards Truly Zero-Shot Compositional Visual Reasoning with LLMs as Programmers Aleksandar Stanić, Sergi Caelles, Michael Tschannen

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Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism Hippolyte Gisserot-Boukhlef, Manuel Faysse, Emmanuel Malherbe, Celine Hudelot, Pierre Colombo

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Towards Unbiased Calibration Using Meta-Regularization Cheng Wang, Jacek Golebiowski

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Towards Understanding Adversarial Transferability in Federated Learning Yijiang Li, Ying Gao, Haohan Wang

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Towards Understanding Dual BN in Hybrid Adversarial Training Chenshuang Zhang, Chaoning Zhang, Kang Zhang, Axi Niu, Junmo Kim, In So Kweon

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Towards Understanding Variants of Invariant Risk Minimization Through the Lens of Calibration Kotaro Yoshida, Hiroki Naganuma

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Training Graph Neural Networks Subject to a Tight Lipschitz Constraint Simona Ioana Juvina, Ana Antonia Neacșu, Jérôme Rony, Jean-Christophe Pesquet, Corneliu Burileanu, Ismail Ben Ayed

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Training LLMs over Neurally Compressed Text Brian Lester, Jaehoon Lee, Alexander A Alemi, Jeffrey Pennington, Adam Roberts, Jascha Sohl-Dickstein, Noah Constant

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Training-Free Graph Neural Networks and the Power of Labels as Features Ryoma Sato

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Training-Free Linear Image Inverses via Flows Ashwini Pokle, Matthew J. Muckley, Ricky T. Q. Chen, Brian Karrer

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Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces Zhou Fan, Xinran Han, Zi Wang

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Transfer Learning for High-Dimensional Quantile Regression with Statistical Guarantee Sheng Qiao, Yong He, Wenxin Zhou

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Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported Ethan Harvey, Mikhail Petrov, Michael C Hughes

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Transformer Architecture Search for Improving Out-of-Domain Generalization in Machine Translation Yiheng He, Ruiyi Zhang, Sai Ashish Somayajula, Pengtao Xie

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Transformer-Based Models Are Not yet Perfect at Learning to Emulate Structural Recursion Dylan Zhang, Curt Tigges, Zory Zhang, Stella Biderman, Maxim Raginsky, Talia Ringer

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Tree Ensembles for Contextual Bandits Hannes Nilsson, Rikard Johansson, Niklas Åkerblom, Morteza Haghir Chehreghani

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Trusted Aggregation (TAG): Backdoor Defense in Federated Learning Joseph Lavond, Minhao Cheng, Yao Li

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Tweedie Moment Projected Diffusions for Inverse Problems Benjamin Boys, Mark Girolami, Jakiw Pidstrigach, Sebastian Reich, Alan Mosca, Omer Deniz Akyildiz

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Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs Angelica Chen, Jason Phang, Alicia Parrish, Vishakh Padmakumar, Chen Zhao, Samuel R. Bowman, Kyunghyun Cho

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UCB Exploration for Fixed-Budget Bayesian Best Arm Identification Rong J.B. Zhu, Yanqi Qiu

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Uncertainty in Graph Neural Networks: A Survey Fangxin Wang, Yuqing Liu, Kay Liu, Yibo Wang, Sourav Medya, Philip S. Yu

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Uncovering Sets of Maximum Dissimilarity on Random Process Data Miguel de Carvalho, Gabriel Martos

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Understanding and Improving Transfer Learning of Deep Models via Neural Collapse Xiao Li, Sheng Liu, Jinxin Zhou, Xinyu Lu, Carlos Fernandez-Granda, Zhihui Zhu, Qing Qu

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Understanding Fairness Surrogate Functions in Algorithmic Fairness Wei Yao, Zhanke Zhou, Zhicong Li, Bo Han, Yong Liu

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Understanding Smoothness of Vector Gaussian Processes on Product Spaces Emilio Porcu, Ana Paula Peron, Eugenio Massa, Xavier Emery

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Understanding Sparse Neural Networks from Their Topology via Multipartite Graph Representations Elia Cunegatti, Matteo Farina, Doina Bucur, Giovanni Iacca

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Understanding the Role of Invariance in Transfer Learning Till Speicher, Vedant Nanda, Krishna P. Gummadi

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Understanding the Role of Layer Normalization in Label-Skewed Federated Learning Guojun Zhang, Mahdi Beitollahi, Alex Bie, Xi Chen

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Undetectable Steganography for Language Models Or Zamir

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UniCtrl: Improving the Spatiotemporal Consistency of Text-to-Video Diffusion Models via Training-Free Unified Attention Control Tian Xia, Xuweiyi Chen, Sihan Xu

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Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods El Mahdi Chayti, Martin Jaggi, Nikita Doikov

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Uniformly Distributed Feature Representations for Fair and Robust Learning Kiran Krishnamachari, See-Kiong Ng, Chuan-Sheng Foo

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Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code Ziyin Zhang, Chaoyu Chen, Bingchang Liu, Cong Liao, Zi Gong, Hang Yu, Jianguo Li, Rui Wang

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Universal Functional Regression with Neural Operator Flows Yaozhong Shi, Angela F Gao, Zachary E Ross, Kamyar Azizzadenesheli

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Universal Neurons in GPT2 Language Models Wes Gurnee, Theo Horsley, Zifan Carl Guo, Tara Rezaei Kheirkhah, Qinyi Sun, Will Hathaway, Neel Nanda, Dimitris Bertsimas

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Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation Vaidehi Patil, Yi-Lin Sung, Peter Hase, Jie Peng, Tianlong Chen, Mohit Bansal

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Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization Jiayu Zhao, Renyu Yang, Shenghao Qiu, Zheng Wang

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Unleashing the Power of Visual Prompting at the Pixel Level Junyang Wu, Xianhang Li, Chen Wei, Huiyu Wang, Alan Yuille, Yuyin Zhou, Cihang Xie

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Unmasking the Veil: An Investigation into Concept Ablation for Privacy and Copyright Protection in Images Shivank Garg, Manyana Tiwari

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Unsupervised 3D Scene Representation Learning via Movable Object Inference Honglin Chen, Wanhee Lee, Hong-Xing Yu, Rahul Mysore Venkatesh, Joshua B. Tenenbaum, Daniel Bear, Jiajun Wu, Daniel LK Yamins

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Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled Shengchao Liu, Chengpeng Wang, Jiarui Lu, Weili Nie, Hanchen Wang, Zhuoxinran Li, Bolei Zhou, Jian Tang

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Unsupervised Domain Adaptation by Learning Using Privileged Information Adam Breitholtz, Anton Matsson, Fredrik D. Johansson

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Unsupervised Training of Convex Regularizers Using Maximum Likelihood Estimation Hong Ye Tan, Ziruo Cai, Marcelo Pereyra, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb

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Unveiling Adversarially Robust Graph Lottery Tickets Subhajit Dutta Chowdhury, Zhiyu Ni, Qingyuan Peng, Souvik Kundu, Pierluigi Nuzzo

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UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation Junhong Shen, Tanya Marwah, Ameet Talwalkar

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Using Motion Cues to Supervise Single-Frame Body Pose & Shape Estimation in Low Data Regimes Andrey Davydov, Alexey Sidnev, Artsiom Sanakoyeu, Yuhua Chen, Mathieu Salzmann, Pascal Fua

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Using Skew to Assess the Quality of GAN-Generated Image Features Lorenzo Luzi, Helen Jenne, Carlos Ortiz Marrero, Ryan Murray

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Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning Mohamadsadegh Khosravani, Sandra Zilles

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Variance-Aware Decision Making with Linear Function Approximation Under Heavy-Tailed Rewards Xiang Li, Qiang Sun

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Variational Autoencoder with Weighted Samples for High-Dimensional Non-Parametric Adaptive Importance Sampling Julien Demange-Chryst, Francois Bachoc, Jérôme Morio, Timothé Krauth

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Variational Autoencoding of Dental Point Clouds Johan Ziruo Ye, Thomas Ørkild, Peter Lempel Søndergard, Søren Hauberg

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Variational Bayesian Imaging with an Efficient Surrogate Score-Based Prior Berthy Feng, Katherine Bouman

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Variational Classification: A Probabilistic Generalization of the SoftMax Classifier Shehzaad Zuzar Dhuliawala, Mrinmaya Sachan, Carl Allen

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Variational Excess Risk Bound for General State Space Models Elisabeth Gassiat, Sylvain Le Corff

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Variational Inference on the Final-Layer Output of Neural Networks Yadi Wei, Roni Khardon

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Variational Learning ISTA Fabio Valerio Massoli, Christos Louizos, Arash Behboodi

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Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics Hanming Yang, Antonio Khalil Moretti, Sebastian Macaluso, Philippe Chlenski, Christian A. Naesseth, Itsik Pe'er

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VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing Paul Couairon, Clément Rambour, Jean-Emmanuel Haugeard, Nicolas Thome

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Video Diffusion Models: A Survey Andrew Melnik, Michal Ljubljanac, Cong Lu, Qi Yan, Weiming Ren, Helge Ritter

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VideoGLUE: Video General Understanding Evaluation of Foundation Models Liangzhe Yuan, Nitesh Bharadwaj Gundavarapu, Long Zhao, Hao Zhou, Yin Cui, Lu Jiang, Xuan Yang, Menglin Jia, Tobias Weyand, Luke Friedman, Mikhail Sirotenko, Huisheng Wang, Florian Schroff, Hartwig Adam, Ming-Hsuan Yang, Ting Liu, Boqing Gong

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Vision Learners Meet Web Image-Text Pairs Bingchen Zhao, Quan Cui, Hao Wu, Osamu Yoshie, Cheng Yang, Oisin Mac Aodha

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Vision-and-Language Navigation Today and Tomorrow: A Survey in the Era of Foundation Models Yue Zhang, Ziqiao Ma, Jialu Li, Yanyuan Qiao, Zun Wang, Joyce Chai, Qi Wu, Mohit Bansal, Parisa Kordjamshidi

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Vision-Language Dataset Distillation Xindi Wu, Byron Zhang, Zhiwei Deng, Olga Russakovsky

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Vision-Language Instruction Tuning: A Review and Analysis Chen Li, Yixiao Ge, Dian Li, Ying Shan

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VisionAD, a Software Package of Performant Anomaly Detection Algorithms, and Proportion Localised, an Interpretable Metric Alexander D. J. Taylor, Phillip Tregidgo, Jonathan James Morrison, Neill D. F. Campbell

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Visual Prompt Based Personalized Federated Learning Guanghao Li, Wansen Wu, Yan Sun, Li Shen, Baoyuan Wu, Dacheng Tao

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Voyager: An Open-Ended Embodied Agent with Large Language Models Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar

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Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits Yi Shen, Pan Xu, Michael Zavlanos

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WaveBench: Benchmarking Data-Driven Solvers for Linear Wave Propagation PDEs Tianlin Liu, Jose Antonio Lara Benitez, Florian Faucher, AmirEhsan Khorashadizadeh, Maarten V. de Hoop, Ivan Dokmanić

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Wavelet Networks: Scale-Translation Equivariant Learning from Raw Time-Series David W. Romero, Erik J Bekkers, Jakub M. Tomczak, Mark Hoogendoorn

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We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline Simar Kareer, Vivek Vijaykumar, Harsh Maheshwari, Judy Hoffman, Prithvijit Chattopadhyay, Viraj Uday Prabhu

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Weighted L1 and L0 Regularization Using Proximal Operator Splitting Methods Zewude A. Berkessa, Patrik Waldmann

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Weighted Risk Invariance: Domain Generalization Under Invariant Feature Shift Gina Wong, Joshua Gleason, Rama Chellappa, Yoav Wald, Anqi Liu

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What Do Larger Image Classifiers Memorise? Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar

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What Does SoftMax Probability Tell Us About Classifiers Ranking Across Diverse Test Conditions? Weijie Tu, Weijian Deng, Liang Zheng, Tom Gedeon

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What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation Within Neighborhood Context Jing Wang, Wonho Bae, Jiahong Chen, Kuangen Zhang, Leonid Sigal, Clarence W. de Silva

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What Is the Solution for State-Adversarial Multi-Agent Reinforcement Learning? Songyang Han, Sanbao Su, Sihong He, Shuo Han, Haizhao Yang, Shaofeng Zou, Fei Miao

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When Is Momentum Extragradient Optimal? a Polynomial-Based Analysis Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis, Fabian Pedregosa

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When Low-Vision Task Meets Dense Prediction Tasks with Less Data: An Auxiliary Self-Trained Geometry Regularization Zaiwang Gu, Weide Liu, Xulei Yang, Chuan-Sheng Foo, Jun Cheng

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When Stability Meets Sufficiency: Informative Explanations That Do Not Overwhelm Ronny Luss, Amit Dhurandhar

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Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark Jan Tönshoff, Martin Ritzert, Eran Rosenbluth, Martin Grohe

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Why Fine-Grained Labels in Pretraining Benefit Generalization? Guan Zhe Hong, Yin Cui, Ariel Fuxman, Stanley H. Chan, Enming Luo

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Why Should Autoencoders Work? Matthew Kvalheim, Eduardo Sontag

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World Models via Policy-Guided Trajectory Diffusion Marc Rigter, Jun Yamada, Ingmar Posner

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XAI-Based Detection of Adversarial Attacks on Deepfake Detectors Ben Pinhasov, Raz Lapid, Rony Ohayon, Moshe Sipper, Yehudit Aperstein

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XAudit : A Learning-Theoretic Look at Auditing with Explanations Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri

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XPL: A Cross-Model Framework for Semi-Supervised Prompt Learning in Vision-Language Models Omprakash Chakraborty, Aadarsh Sahoo, Rameswar Panda, Abir Das

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Your Classifier Can Be Secretly a Likelihood-Based OOD Detector Jirayu Burapacheep, Yixuan Li

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Zero-Order One-Point Gradient Estimate in Consensus-Based Distributed Stochastic Optimization Elissa Mhanna, Mohamad Assaad

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ZigZag: Universal Sampling-Free Uncertainty Estimation Through Two-Step Inference Nikita Durasov, Nik Dorndorf, Hieu Le, Pascal Fua

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Λ-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space Maitreya Patel, Sangmin Jung, Chitta Baral, Yezhou Yang

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