ICML 2023
1827 papers
2D-Shapley: A Framework for Fragmented Data Valuation
Zhihong Liu, Hoang Anh Just, Xiangyu Chang, Xi Chen, Ruoxi Jia A Closer Look at Few-Shot Classification Again
Xu Luo, Hao Wu, Ji Zhang, Lianli Gao, Jing Xu, Jingkuan Song A Coupled Flow Approach to Imitation Learning
Gideon Joseph Freund, Elad Sarafian, Sarit Kraus A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment
Hanchen Xie, Jiageng Zhu, Mahyar Khayatkhoei, Jiazhi Li, Mohamed E. Hussein, Wael Abdalmageed A Deep Conjugate Direction Method for Iteratively Solving Linear Systems
Ayano Kaneda, Osman Akar, Jingyu Chen, Victoria Alicia Trevino Kala, David Hyde, Joseph Teran A Flexible Diffusion Model
Weitao Du, He Zhang, Tao Yang, Yuanqi Du A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
Oliver Slumbers, David Henry Mguni, Stefano B Blumberg, Stephen Marcus Mcaleer, Yaodong Yang, Jun Wang A Generalization of ViT/MLP-Mixer to Graphs
Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann Lecun, Xavier Bresson A Kernel-Based View of Language Model Fine-Tuning
Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, Sanjeev Arora A Modern Look at the Relationship Between Sharpness and Generalization
Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion A Picture of the Space of Typical Learnable Tasks
Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang, Han Kheng Teoh, Mark Transtrum, James Sethna, Pratik Chaudhari A Simple Zero-Shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models
James Urquhart Allingham, Jie Ren, Michael W Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan A Study on Transformer Configuration and Training Objective
Fuzhao Xue, Jianghai Chen, Aixin Sun, Xiaozhe Ren, Zangwei Zheng, Xiaoxin He, Yongming Chen, Xin Jiang, Yang You A Theory of Continuous Generative Flow Networks
Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-Garcı́a, Lena Nehale Ezzine, Yoshua Bengio, Nikolay Malkin A Theory of Representation Learning Gives a Deep Generalisation of Kernel Methods
Adam X. Yang, Maxime Robeyns, Edward Milsom, Ben Anson, Nandi Schoots, Laurence Aitchison A Three-Regime Model of Network Pruning
Yefan Zhou, Yaoqing Yang, Arin Chang, Michael W. Mahoney A Universal Unbiased Method for Classification from Aggregate Observations
Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen A Watermark for Large Language Models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein Abstracting Imperfect Information Away from Two-Player Zero-Sum Games
Samuel Sokota, Ryan D’Orazio, Chun Kai Ling, David J Wu, J Zico Kolter, Noam Brown ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging
Alessandro Fontanella, Antreas Antoniou, Wenwen Li, Joanna Wardlaw, Grant Mair, Emanuele Trucco, Amos Storkey Action Matching: Learning Stochastic Dynamics from Samples
Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani Active Causal Structure Learning with Advice
Davin Choo, Themistoklis Gouleakis, Arnab Bhattacharyya Active Policy Improvement from Multiple Black-Box Oracles
Xuefeng Liu, Takuma Yoneda, Chaoqi Wang, Matthew Walter, Yuxin Chen AdaBoost Is Not an Optimal Weak to Strong Learner
Mikael Møller Høgsgaard, Kasper Green Larsen, Martin Ritzert AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
Yifan Zhang, Xue Wang, Kexin Jin, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan AdaptDiffuser: Diffusion Models as Adaptive Self-Evolving Planners
Zhixuan Liang, Yao Mu, Mingyu Ding, Fei Ni, Masayoshi Tomizuka, Ping Luo Adapting to Game Trees in Zero-Sum Imperfect Information Games
Côme Fiegel, Pierre Menard, Tadashi Kozuno, Remi Munos, Vianney Perchet, Michal Valko Adaptive Compositional Continual Meta-Learning
Bin Wu, Jinyuan Fang, Xiangxiang Zeng, Shangsong Liang, Qiang Zhang Adaptive Computation with Elastic Input Sequence
Fuzhao Xue, Valerii Likhosherstov, Anurag Arnab, Neil Houlsby, Mostafa Dehghani, Yang You Adaptive Coordination in Social Embodied Rearrangement
Andrew Szot, Unnat Jain, Dhruv Batra, Zsolt Kira, Ruta Desai, Akshara Rai Adaptive Estimation of Graphical Models Under Total Positivity
Jiaxi Ying, José Vinı́cius De Miranda Cardoso, Daniel P. Palomar Adaptive Whitening in Neural Populations with Gain-Modulating Interneurons
Lyndon Duong, David Lipshutz, David Heeger, Dmitri Chklovskii, Eero P Simoncelli Additive Causal Bandits with Unknown Graph
Alan Malek, Virginia Aglietti, Silvia Chiappa Adversarial Cheap Talk
Chris Lu, Timon Willi, Alistair Letcher, Jakob Nicolaus Foerster Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
Chumeng Liang, Xiaoyu Wu, Yang Hua, Jiaru Zhang, Yiming Xue, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan Adversarial Policies Beat Superhuman Go AIs
Tony Tong Wang, Adam Gleave, Tom Tseng, Kellin Pelrine, Nora Belrose, Joseph Miller, Michael D Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell Algorithmic Collective Action in Machine Learning
Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic Aligning Language Models with Preferences Through $f$-Divergence Minimization
Dongyoung Go, Tomasz Korbak, Germàn Kruszewski, Jos Rozen, Nahyeon Ryu, Marc Dymetman All in a Row: Compressed Convolution Networks for Graphs
Junshu Sun, Shuhui Wang, Xinzhe Han, Zhe Xue, Qingming Huang Alternately Optimized Graph Neural Networks
Haoyu Han, Xiaorui Liu, Haitao Mao, Mohamadali Torkamani, Feng Shi, Victor Lee, Jiliang Tang An Effective Meaningful Way to Evaluate Survival Models
Shi-Ang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner An SDE for Modeling SAM: Theory and Insights
Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurelien Lucchi Analyzing Diffusion as Serial Reproduction
Raja Marjieh, Ilia Sucholutsky, Thomas A Langlois, Nori Jacoby, Thomas L. Griffiths Anti-Exploration by Random Network Distillation
Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, Sergey Kolesnikov Applied Online Algorithms with Heterogeneous Predictors
Jessica Maghakian, Russell Lee, Mohammad Hajiesmaili, Jian Li, Ramesh Sitaraman, Zhenhua Liu Approximately Optimal Core Shapes for Tensor Decompositions
Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni Approximation Algorithms for Fair Range Clustering
Sedjro Salomon Hotegni, Sepideh Mahabadi, Ali Vakilian Are Large Kernels Better Teachers than Transformers for ConvNets?
Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
Luke Vilnis, Yury Zemlyanskiy, Patrick Murray, Alexandre Tachard Passos, Sumit Sanghai Attention-Based Recurrence for Multi-Agent Reinforcement Learning Under Stochastic Partial Observability
Thomy Phan, Fabian Ritz, Philipp Altmann, Maximilian Zorn, Jonas Nüßlein, Michael Kölle, Thomas Gabor, Claudia Linnhoff-Popien AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, Mark D Plumbley Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
Yuxin Tang, Zhimin Ding, Dimitrije Jankov, Binhang Yuan, Daniel Bourgeois, Chris Jermaine Autoregressive Diffusion Model for Graph Generation
Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, Chao Zhang Auxiliary Learning as an Asymmetric Bargaining Game
Aviv Shamsian, Aviv Navon, Neta Glazer, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya Bag of Tricks for Training Data Extraction from Language Models
Weichen Yu, Tianyu Pang, Qian Liu, Chao Du, Bingyi Kang, Yan Huang, Min Lin, Shuicheng Yan Bandit Online Linear Optimization with Hints and Queries
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit Bayesian Estimation of Differential Privacy
Santiago Zanella-Beguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Rühle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones Beam Tree Recursive Cells
Jishnu Ray Chowdhury, Cornelia Caragea BEATs: Audio Pre-Training with Acoustic Tokenizers
Sanyuan Chen, Yu Wu, Chengyi Wang, Shujie Liu, Daniel Tompkins, Zhuo Chen, Wanxiang Che, Xiangzhan Yu, Furu Wei Behavior Contrastive Learning for Unsupervised Skill Discovery
Rushuai Yang, Chenjia Bai, Hongyi Guo, Siyuan Li, Bin Zhao, Zhen Wang, Peng Liu, Xuelong Li Benign Overfitting in Deep Neural Networks Under Lazy Training
Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Francesco Locatello, Volkan Cevher Best of Both Worlds Policy Optimization
Christoph Dann, Chen-Yu Wei, Julian Zimmert Better Diffusion Models Further Improve Adversarial Training
Zekai Wang, Tianyu Pang, Chao Du, Min Lin, Weiwei Liu, Shuicheng Yan Beyond In-Domain Scenarios: Robust Density-Aware Calibration
Christian Tomani, Futa Kai Waseda, Yuesong Shen, Daniel Cremers Bi-Directional Masks for Efficient N:M Sparse Training
Yuxin Zhang, Yiting Luo, Mingbao Lin, Yunshan Zhong, Jingjing Xie, Fei Chao, Rongrong Ji BiBench: Benchmarking and Analyzing Network Binarization
Haotong Qin, Mingyuan Zhang, Yifu Ding, Aoyu Li, Zhongang Cai, Ziwei Liu, Fisher Yu, Xianglong Liu Bigger, Better, Faster: Human-Level Atari with Human-Level Efficiency
Max Schwarzer, Johan Samir Obando Ceron, Aaron Courville, Marc G Bellemare, Rishabh Agarwal, Pablo Samuel Castro BiRT: Bio-Inspired Replay in Vision Transformers for Continual Learning
Kishaan Jeeveswaran, Prashant Shivaram Bhat, Bahram Zonooz, Elahe Arani Bit Allocation Using Optimization
Tongda Xu, Han Gao, Chenjian Gao, Yuanyuan Wang, Dailan He, Jinyong Pi, Jixiang Luo, Ziyu Zhu, Mao Ye, Hongwei Qin, Yan Wang, Jingjing Liu, Ya-Qin Zhang Boosting Offline Reinforcement Learning with Action Preference Query
Qisen Yang, Shenzhi Wang, Matthieu Gaetan Lin, Shiji Song, Gao Huang Bootstrapped Representations in Reinforcement Learning
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G Bellemare, Will Dabney Brainformers: Trading Simplicity for Efficiency
Yanqi Zhou, Nan Du, Yanping Huang, Daiyi Peng, Chang Lan, Da Huang, Siamak Shakeri, David So, Andrew M. Dai, Yifeng Lu, Zhifeng Chen, Quoc V Le, Claire Cui, James Laudon, Jeff Dean Calibrating Multimodal Learning
Huan Ma, Qingyang Zhang, Changqing Zhang, Bingzhe Wu, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu Can Forward Gradient Match Backpropagation?
Louis Fournier, Stephane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon Can Large Language Models Reason About Program Invariants?
Kexin Pei, David Bieber, Kensen Shi, Charles Sutton, Pengcheng Yin Can Neural Network Memorization Be Localized?
Pratyush Maini, Michael Curtis Mozer, Hanie Sedghi, Zachary Chase Lipton, J Zico Kolter, Chiyuan Zhang Causal Isotonic Calibration for Heterogeneous Treatment Effects
Lars Van Der Laan, Ernesto Ulloa-Perez, Marco Carone, Alex Luedtke Causal Modeling of Policy Interventions from Treatment-Outcome Sequences
Çağlar Hızlı, S. T. John, Anne Tuulikki Juuti, Tuure Tapani Saarinen, Kirsi Hannele Pietiläinen, Pekka Marttinen Causal Proxy Models for Concept-Based Model Explanations
Zhengxuan Wu, Karel D’Oosterlinck, Atticus Geiger, Amir Zur, Christopher Potts Cell-Free Latent Go-Explore
Quentin Gallouédec, Emmanuel Dellandrea Certifying Ensembles: A General Certification Theory with S-Lipschitzness
Aleksandar Petrov, Francisco Eiras, Amartya Sanyal, Philip Torr, Adel Bibi Change Is Hard: A Closer Look at Subpopulation Shift
Yuzhe Yang, Haoran Zhang, Dina Katabi, Marzyeh Ghassemi CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets
Zachary Novack, Julian Mcauley, Zachary Chase Lipton, Saurabh Garg CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling
Yansen Wang, Xinyang Jiang, Kan Ren, Caihua Shan, Xufang Luo, Dongqi Han, Kaitao Song, Yifei Shen, Dongsheng Li ClimaX: A Foundation Model for Weather and Climate
Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K Gupta, Aditya Grover CLIPood: Generalizing CLIP to Out-of-Distributions
Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang, Mingsheng Long CLUSTSEG: Clustering for Universal Segmentation
James Chenhao Liang, Tianfei Zhou, Dongfang Liu, Wenguan Wang CLUTR: Curriculum Learning via Unsupervised Task Representation Learning
Abdus Salam Azad, Izzeddin Gur, Jasper Emhoff, Nathaniel Alexis, Aleksandra Faust, Pieter Abbeel, Ion Stoica Coarse-to-Fine: A Hierarchical Diffusion Model for Molecule Generation in 3D
Bo Qiang, Yuxuan Song, Minkai Xu, Jingjing Gong, Bowen Gao, Hao Zhou, Wei-Ying Ma, Yanyan Lan CocktailSGD: Fine-Tuning Foundation Models over 500Mbps Networks
Jue Wang, Yucheng Lu, Binhang Yuan, Beidi Chen, Percy Liang, Christopher De Sa, Christopher Re, Ce Zhang CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models
Zhiyuan Yu, Yuhao Wu, Ning Zhang, Chenguang Wang, Yevgeniy Vorobeychik, Chaowei Xiao Coder Reviewer Reranking for Code Generation
Tianyi Zhang, Tao Yu, Tatsunori Hashimoto, Mike Lewis, Wen-Tau Yih, Daniel Fried, Sida Wang Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
Ronshee Chawla, Daniel Vial, Sanjay Shakkottai, R. Srikant Combinatorial Neural Bandits
Taehyun Hwang, Kyuwook Chai, Min-Hwan Oh Competitive Gradient Optimization
Abhijeet Vyas, Brian Bullins, Kamyar Azizzadenesheli Complementary Attention for Multi-Agent Reinforcement Learning
Jianzhun Shao, Hongchang Zhang, Yun Qu, Chang Liu, Shuncheng He, Yuhang Jiang, Xiangyang Ji Compositional Exemplars for In-Context Learning
Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, Lingpeng Kong Concept-Based Explanations for Out-of-Distribution Detectors
Jihye Choi, Jayaram Raghuram, Ryan Feng, Jiefeng Chen, Somesh Jha, Atul Prakash Conditional Graph Information Bottleneck for Molecular Relational Learning
Namkyeong Lee, Dongmin Hyun, Gyoung S. Na, Sungwon Kim, Junseok Lee, Chanyoung Park Conditionally Strongly Log-Concave Generative Models
Florentin Guth, Etienne Lempereur, Joan Bruna, Stéphane Mallat Cones: Concept Neurons in Diffusion Models for Customized Generation
Zhiheng Liu, Ruili Feng, Kai Zhu, Yifei Zhang, Kecheng Zheng, Yu Liu, Deli Zhao, Jingren Zhou, Yang Cao Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov Conformal Prediction Sets for Graph Neural Networks
Soroush H. Zargarbashi, Simone Antonelli, Aleksandar Bojchevski Conformal Prediction with Missing Values
Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano Consistency Models
Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever Consistency of Multiple Kernel Clustering
Weixuan Liang, Xinwang Liu, Yong Liu, Chuan Ma, Yunping Zhao, Zhe Liu, En Zhu Constrained Causal Bayesian Optimization
Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa Constrained Decision Transformer for Offline Safe Reinforcement Learning
Zuxin Liu, Zijian Guo, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, Ding Zhao Constrained Monotonic Neural Networks
Davor Runje, Sharath M Shankaranarayana Constrained Phi-Equilibria
Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Francesco Trovò, Nicola Gatti Context Consistency Regularization for Label Sparsity in Time Series
Yooju Shin, Susik Yoon, Hwanjun Song, Dongmin Park, Byunghyun Kim, Jae-Gil Lee, Byung Suk Lee Contextual Combinatorial Bandits with Probabilistically Triggered Arms
Xutong Liu, Jinhang Zuo, Siwei Wang, John C.S. Lui, Mohammad Hajiesmaili, Adam Wierman, Wei Chen Contextual Reliability: When Different Features Matter in Different Contexts
Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca Dragan, Aditi Raghunathan Continual Learning in Linear Classification on Separable Data
Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry Continuous Spatiotemporal Transformer
Antonio Henrique De Oliveira Fonseca, Emanuele Zappala, Josue Ortega Caro, David Van Dijk Continuously Parameterized Mixture Models
Christopher M Bender, Yifeng Shi, Marc Niethammer, Junier Oliva Contrastive Learning Meets Homophily: Two Birds with One Stone
Dongxiao He, Jitao Zhao, Rui Guo, Zhiyong Feng, Di Jin, Yuxiao Huang, Zhen Wang, Weixiong Zhang Controllability-Aware Unsupervised Skill Discovery
Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel Controllable Neural Symbolic Regression
Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny Controlled Text Generation with Natural Language Instructions
Wangchunshu Zhou, Yuchen Eleanor Jiang, Ethan Wilcox, Ryan Cotterell, Mrinmaya Sachan Cooperative Open-Ended Learning Framework for Zero-Shot Coordination
Yang Li, Shao Zhang, Jichen Sun, Yali Du, Ying Wen, Xinbing Wang, Wei Pan Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets
Yurong Chen, Qian Wang, Zhijian Duan, Haoran Sun, Zhaohua Chen, Xiang Yan, Xiaotie Deng CRISP: Curriculum Based Sequential Neural Decoders for Polar Code Family
S Ashwin Hebbar, Viraj Vivek Nadkarni, Ashok Vardhan Makkuva, Suma Bhat, Sewoong Oh, Pramod Viswanath Cross-Modal Fine-Tuning: Align Then Refine
Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments
Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Remi Munos, Michal Valko Curious Replay for Model-Based Adaptation
Isaac Kauvar, Chris Doyle, Linqi Zhou, Nick Haber Cut Your Losses with Squentropy
Like Hui, Mikhail Belkin, Stephen Wright Data Poisoning Attacks Against Multimodal Encoders
Ziqing Yang, Xinlei He, Zheng Li, Michael Backes, Mathias Humbert, Pascal Berrang, Yang Zhang Data Structures for Density Estimation
Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal Data-Copying in Generative Models: A Formal Framework
Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu Deep Anomaly Detection Under Labeling Budget Constraints
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, Marco Virgolin Deep Graph Representation Learning and Optimization for Influence Maximization
Chen Ling, Junji Jiang, Junxiang Wang, My T. Thai, Renhao Xue, James Song, Meikang Qiu, Liang Zhao Deep Latent State Space Models for Time-Series Generation
Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, Stefano Ermon Deep Regression Unlearning
Ayush Kumar Tarun, Vikram Singh Chundawat, Murari Mandal, Mohan Kankanhalli Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery
Dingrong Wang, Deep Shankar Pandey, Krishna Prasad Neupane, Zhiwei Yu, Ervine Zheng, Zhi Zheng, Qi Yu Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Re, Beidi Chen Delay-Agnostic Asynchronous Coordinate Update Algorithm
Xuyang Wu, Changxin Liu, Sindri Magnússon, Mikael Johansson Delayed Bandits: When Do Intermediate Observations Help?
Emmanuel Esposito, Saeed Masoudian, Hao Qiu, Dirk Van Der Hoeven, Nicolò Cesa-Bianchi, Yevgeny Seldin Delayed Feedback in Kernel Bandits
Sattar Vakili, Danyal Ahmed, Alberto Bernacchia, Ciara Pike-Burke Demystifying Disagreement-on-the-Line in High Dimensions
Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani DetectGPT: Zero-Shot Machine-Generated Text Detection Using Probability Curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D Manning, Chelsea Finn Detecting Out-of-Distribution Data Through In-Distribution Class Prior
Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han Differentiable and Transportable Structure Learning
Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela Van Der Schaar Differentiable Multi-Target Causal Bayesian Experimental Design
Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer Differentiable Simulations for Enhanced Sampling of Rare Events
Martin Sipka, Johannes C. B. Dietschreit, Lukáš Grajciar, Rafael Gomez-Bombarelli Differentiable Tree Operations Promote Compositional Generalization
Paul Soulos, Edward J Hu, Kate Mccurdy, Yunmo Chen, Roland Fernandez, Paul Smolensky, Jianfeng Gao Differentially Private Hierarchical Clustering with Provable Approximation Guarantees
Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni Differentially Private Sharpness-Aware Training
Jinseong Park, Hoki Kim, Yujin Choi, Jaewook Lee Diffusion Based Representation Learning
Sarthak Mittal, Korbinian Abstreiter, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou Diffusion Models as Artists: Are We Closing the Gap Between Humans and Machines?
Victor Boutin, Thomas Fel, Lakshya Singhal, Rishav Mukherji, Akash Nagaraj, Julien Colin, Thomas Serre Diffusion Models for Black-Box Optimization
Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover Dink-Net: Neural Clustering on Large Graphs
Yue Liu, Ke Liang, Jun Xia, Sihang Zhou, Xihong Yang, Xinwang Liu, Stan Z. Li DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design
Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab Discovering Object-Centric Generalized Value Functions from Pixels
Somjit Nath, Gopeshh Subbaraj, Khimya Khetarpal, Samira Ebrahimi Kahou Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models
Parth Vipul Sangani, Arjun Shashank Kashettiwar, Pritish Chakraborty, Bhuvan Reddy Gangula, Durga S, Ganesh Ramakrishnan, Rishabh K Iyer, Abir De Discrete Key-Value Bottleneck
Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Curtis Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf Disentangled Generative Models for Robust Prediction of System Dynamics
Stathi Fotiadis, Mario Lino Valencia, Shunlong Hu, Stef Garasto, Chris D Cantwell, Anil Anthony Bharath Disentangled Multi-Fidelity Deep Bayesian Active Learning
Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yian Ma, Rose Yu Disentangled Multiplex Graph Representation Learning
Yujie Mo, Yajie Lei, Jialie Shen, Xiaoshuang Shi, Heng Tao Shen, Xiaofeng Zhu Distilling Internet-Scale Vision-Language Models into Embodied Agents
Theodore Sumers, Kenneth Marino, Arun Ahuja, Rob Fergus, Ishita Dasgupta Distribution Free Domain Generalization
Peifeng Tong, Wu Su, He Li, Jialin Ding, Zhan Haoxiang, Song Xi Chen Diversity-Enhancing Generative Network for Few-Shot Hypothesis Adaptation
Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han DIVISION: Memory Efficient Training via Dual Activation Precision
Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou, Xia Hu Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making Using Language Guided World Modelling
Kolby Nottingham, Prithviraj Ammanabrolu, Alane Suhr, Yejin Choi, Hannaneh Hajishirzi, Sameer Singh, Roy Fox Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark
Alexander Pan, Jun Shern Chan, Andy Zou, Nathaniel Li, Steven Basart, Thomas Woodside, Hanlin Zhang, Scott Emmons, Dan Hendrycks DoCoFL: Downlink Compression for Cross-Device Federated Learning
Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Yehuda Levy Does Continual Learning Equally Forget All Parameters?
Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang Domain Adaptation for Time Series Under Feature and Label Shifts
Huan He, Owen Queen, Teddy Koker, Consuelo Cuevas, Theodoros Tsiligkaridis, Marinka Zitnik DoMo-AC: Doubly Multi-Step Off-Policy Actor-Critic Algorithm
Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Remi Munos, Bernardo Avila Pires, Michal Valko Double-Weighting for Covariate Shift Adaptation
José I. Segovia-Martín, Santiago Mazuelas, Anqi Liu DRCFS: Doubly Robust Causal Feature Selection
Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Cristian R. Rojas, Stefan Bauer DRew: Dynamically Rewired Message Passing with Delay
Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni Dropout Reduces Underfitting
Zhuang Liu, Zhiqiu Xu, Joseph Jin, Zhiqiang Shen, Trevor Darrell Drug Discovery Under Covariate Shift with Domain-Informed Prior Distributions over Functions
Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M Morris, Charlotte Deane, Yee Whye Teh DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation
Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Wen-Tau Yih, Daniel Fried, Sida Wang, Tao Yu Dual Focal Loss for Calibration
Linwei Tao, Minjing Dong, Chang Xu DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning
Zifeng Wang, Zheng Zhan, Yifan Gong, Yucai Shao, Stratis Ioannidis, Yanzhi Wang, Jennifer Dy DUET: 2D Structured and Approximately Equivariant Representations
Xavier Suau, Federico Danieli, T. Anderson Keller, Arno Blaas, Chen Huang, Jason Ramapuram, Dan Busbridge, Luca Zappella dugMatting: Decomposed-Uncertainty-Guided Matting
Jiawei Wu, Changqing Zhang, Zuoyong Li, Huazhu Fu, Xi Peng, Joey Tianyi Zhou Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time
Kiarash Banihashem, Leyla Biabani, Samira Goudarzi, Mohammadtaghi Hajiaghayi, Peyman Jabbarzade, Morteza Monemizadeh Dynamical Linear Bandits
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André Susano Pinto, Alexander Kolesnikov, Yuge Shi, Lucas Beyer, Xiaohua Zhai Uncertainty Estimation for Molecules: Desiderata and Methods
Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann Uncovering Adversarial Risks of Test-Time Adaptation
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Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Avila Pires, Razvan Pascanu, Will Dabney Understanding Self-Predictive Learning for Reinforcement Learning
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Ali Siahkoohi, Rudy Morel, Maarten V. De Hoop, Erwan Allys, Gregory Sainton, Taichi Kawamura Unifying Molecular and Textual Representations via Multi-Task Language Modelling
Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, Matteo Manica Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability
Jianing Zhu, Hengzhuang Li, Jiangchao Yao, Tongliang Liu, Jianliang Xu, Bo Han Unlocking Slot Attention by Changing Optimal Transport Costs
Yan Zhang, David W. Zhang, Simon Lacoste-Julien, Gertjan J. Burghouts, Cees G. M. Snoek Unscented Autoencoder
Faris Janjos, Lars Rosenbaum, Maxim Dolgov, J. Marius Zoellner Unveiling the Mask of Position-Information Pattern Through the Mist of Image Features
Chieh Hubert Lin, Hung-Yu Tseng, Hsin-Ying Lee, Maneesh Kumar Singh, Ming-Hsuan Yang UPSCALE: Unconstrained Channel Pruning
Alvin Wan, Hanxiang Hao, Kaushik Patnaik, Yueyang Xu, Omer Hadad, David Güera, Zhile Ren, Qi Shan Variational Autoencoding Neural Operators
Jacob H Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris Variational Mixture of HyperGenerators for Learning Distributions over Functions
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Valentin Liévin, Andreas Geert Motzfeldt, Ida Riis Jensen, Ole Winther Vector Quantized Wasserstein Auto-Encoder
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Zhuo Sun, Alessandro Barp, Francois-Xavier Briol VectorMapNet: End-to-End Vectorized HD mAP Learning
Yicheng Liu, Tianyuan Yuan, Yue Wang, Yilun Wang, Hang Zhao VIMA: Robot Manipulation with Multimodal Prompts
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Xin Cheng, Yuzhou Cao, Ximing Li, Bo An, Lei Feng When Do Minimax-Fair Learning and Empirical Risk Minimization Coincide?
Harvineet Singh, Matthäus Kleindessner, Volkan Cevher, Rumi Chunara, Chris Russell When Does Privileged Information Explain Away Label Noise?
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