TMLR 2022

216 papers

A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey Elahe Arani, Shruthi Gowda, Ratnajit Mukherjee, Omar Magdy, Senthilkumar Sockalingam Kathiresan, Bahram Zonooz

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A Crisis in Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, Volodimir Begy, Gilles Louppe

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A Generalist Agent Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-maron, Mai Giménez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas

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A Geometrical Connection Between Sparse and Low-Rank Matrices and Its Application to Manifold Learning Lawrence K. Saul

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A Note on "Assessing Generalization of SGD via Disagreement" Andreas Kirsch, Yarin Gal

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A Rigorous Study of the Deep Taylor Decomposition Leon Sixt, Tim Landgraf

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A Self-Supervised Framework for Function Learning and Extrapolation Simon Segert, Jonathan Cohen

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A Simple Convergence Proof of Adam and AdaGrad Alexandre Défossez, Leon Bottou, Francis Bach, Nicolas Usunier

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A Snapshot of the Frontiers of Client Selection in Federated Learning Gergely Dániel Németh, Miguel Angel Lozano, Novi Quadrianto, Nuria M Oliver

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A Stochastic Optimization Framework for Fair Risk Minimization Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami

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A Unified Domain Adaptation Framework with Distinctive Divergence Analysis Zhiri Yuan, Xixu Hu, Qi Wu, Shumin Ma, Cheuk Hang Leung, Xin Shen, Yiyan Huang

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A Unified Survey on Anomaly, Novelty, Open-Set, and Out Of-Distribution Detection: Solutions and Future Challenges Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, Mohammad Sabokrou

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Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance Jakob Hollenstein, Sayantan Auddy, Matteo Saveriano, Erwan Renaudo, Justus Piater

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Adversarial Feature Augmentation and Normalization for Visual Recognition Tianlong Chen, Yu Cheng, Zhe Gan, Jianfeng Wang, Lijuan Wang, Jingjing Liu, Zhangyang Wang

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Algorithms and Theory for Supervised Gradual Domain Adaptation Jing Dong, Shiji Zhou, Baoxiang Wang, Han Zhao

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An Approximate Sampler for Energy-Based Models with Divergence Diagnostics Bryan Eikema, Germán Kruszewski, Christopher R Dance, Hady Elsahar, Marc Dymetman

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An Efficient One-Class SVM for Novelty Detection in IoT Kun Yang, Samory Kpotufe, Nick Feamster

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An Empirical Study of Implicit Regularization in Deep Offline RL Caglar Gulcehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matthew Hoffman, Razvan Pascanu, Arnaud Doucet

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ANCER: Anisotropic Certification via Sample-Wise Volume Maximization Francisco Eiras, Motasem Alfarra, Philip Torr, M. Pawan Kumar, Puneet K. Dokania, Bernard Ghanem, Adel Bibi

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Approximate Policy Iteration with Bisimulation Metrics Mete Kemertas, Allan Douglas Jepson

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Approximating 1-Wasserstein Distance with Trees Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi

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Attentive Walk-Aggregating Graph Neural Networks Mehmet F Demirel, Shengchao Liu, Siddhant Garg, Zhenmei Shi, Yingyu Liang

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Attribute Prediction as Multiple Instance Learning Diego Marcos, Aike Potze, Wenjia Xu, Devis Tuia, Zeynep Akata

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Auto-Lambda: Disentangling Dynamic Task Relationships Shikun Liu, Stephen James, Andrew Davison, Edward Johns

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Bayesian Methods for Constraint Inference in Reinforcement Learning Dimitris Papadimitriou, Usman Anwar, Daniel S. Brown

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Behind the Machine’s Gaze: Neural Networks with Biologically-Inspired Constraints Exhibit Human-like Visual Attention Leo Schwinn, Doina Precup, Bjoern Eskofier, Dario Zanca

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Benchmarking and Analyzing Unsupervised Network Representation Learning and the Illusion of Progress Saket Gurukar, Priyesh Vijayan, Srinivasan Parthasarathy, Balaraman Ravindran, Aakash Srinivasan, Goonmeet Bajaj, Chen Cai, Moniba Keymanesh, Saravana Kumar, Pranav Maneriker, Anasua Mitra, Vedang Patel

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Benchmarking Progress to Infant-Level Physical Reasoning in AI Luca Weihs, Amanda Yuile, Renée Baillargeon, Cynthia Fisher, Gary Marcus, Roozbeh Mottaghi, Aniruddha Kembhavi

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Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation Miao Xiong, Shen Li, Wenjie Feng, Ailin Deng, Jihai Zhang, Bryan Hooi

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Boosting Search Engines with Interactive Agents Leonard Adolphs, Benjamin Börschinger, Christian Buck, Michelle Chen Huebscher, Massimiliano Ciaramita, Lasse Espeholt, Thomas Hofmann, Yannic Kilcher, Sascha Rothe, Pier Giuseppe Sessa, Lierni Sestorain

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Bridging Offline and Online Experimentation: Constraint Active Search for Deployed Performance Optimization Junpei Komiyama, Gustavo Malkomes, Bolong Cheng, Michael McCourt

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Calibrated Selective Classification Adam Fisch, Tommi S. Jaakkola, Regina Barzilay

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Can You Win Everything with a Lottery Ticket? Tianlong Chen, Zhenyu Zhang, Jun Wu, Randy Huang, Sijia Liu, Shiyu Chang, Zhangyang Wang

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Causal Feature Selection via Orthogonal Search Ashkan Soleymani, Anant Raj, Stefan Bauer, Bernhard Schölkopf, Michel Besserve

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Centroids Matching: An Efficient Continual Learning Approach Operating in the Embedding Space Jary Pomponi, Simone Scardapane, Aurelio Uncini

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Clustering Units in Neural Networks: Upstream vs Downstream Information Richard D Lange, David Rolnick, Konrad Kording

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CoCa: Contrastive Captioners Are Image-Text Foundation Models Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, Yonghui Wu

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COIN++: Neural Compression Across Modalities Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet

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Collaborative Algorithms for Online Personalized Mean Estimation Mahsa Asadi, Aurélien Bellet, Odalric-Ambrym Maillard, Marc Tommasi

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Competition over Data: How Does Data Purchase Affect Users? Yongchan Kwon, Tony A Ginart, James Zou

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Completeness and Coherence Learning for Fast Arbitrary Style Transfer Zhijie Wu, Chunjin Song, Guanxiong Chen, Sheng Guo, Weilin Huang

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Complex-Valued Autoencoders for Object Discovery Sindy Löwe, Phillip Lippe, Maja Rudolph, Max Welling

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Concave Utility Reinforcement Learning with Zero-Constraint Violations Mridul Agarwal, Qinbo Bai, Vaneet Aggarwal

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Conformal Prediction Intervals with Temporal Dependence Zhen Lin, Shubhendu Trivedi, Jimeng Sun

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Controllable Generative Modeling via Causal Reasoning Joey Bose, Ricardo Pio Monti, Aditya Grover

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Convergence of Denoising Diffusion Models Under the Manifold Hypothesis Valentin De Bortoli

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Counterfactual Learning with Multioutput Deep Kernels Alberto Caron, Ioanna Manolopoulou, Gianluca Baio

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Data Leakage in Federated Averaging Dimitar Iliev Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin Vechev

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Decoder Denoising Pretraining for Semantic Segmentation Emmanuel Asiedu Brempong, Simon Kornblith, Ting Chen, Niki Parmar, Matthias Minderer, Mohammad Norouzi

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Decoding EEG with Spiking Neural Networks on Neuromorphic Hardware Neelesh Kumar, Guangzhi Tang, Raymond Yoo, Konstantinos P. Michmizos

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Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions That Matter Jaime Spencer, Chris Russell, Simon Hadfield, Richard Bowden

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Deep Classifiers with Label Noise Modeling and Distance Awareness Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou

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Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure Samuel Kim, Peter Y Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic

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Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach Mohammad Ali Alomrani, Reza Moravej, Elias Boutros Khalil

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Deformation Robust Roto-Scale-Translation Equivariant CNNs Liyao Gao, Guang Lin, Wei Zhu

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Degradation Attacks on Certifiably Robust Neural Networks Klas Leino, Chi Zhang, Ravi Mangal, Matt Fredrikson, Bryan Parno, Corina Pasareanu

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DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-Parameter and Architecture Kaichen Zhou, Lanqing Hong, Shoukang Hu, Fengwei Zhou, Binxin Ru, Jiashi Feng, Zhenguo Li

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Diagnosing and Fixing Manifold Overfitting in Deep Generative Models Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C Cresswell, Anthony L. Caterini

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Did I Do That? Blame as a Means to Identify Controlled Effects in Reinforcement Learning Oriol Corcoll, Youssef Sherif Mansour Mohamed, Raul Vicente

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Differentiable Model Compression via Pseudo Quantization Noise Alexandre Défossez, Yossi Adi, Gabriel Synnaeve

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Differentially Private Stochastic Expectation Propagation Margarita Vinaroz, Mijung Park

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DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents Kushagra Pandey, Avideep Mukherjee, Piyush Rai, Abhishek Kumar

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Diffusion Models for Video Prediction and Infilling Tobias Höppe, Arash Mehrjou, Stefan Bauer, Didrik Nielsen, Andrea Dittadi

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Direct Molecular Conformation Generation Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Yusong Wang, Tong Wang, Tao Qin, Wengang Zhou, Houqiang Li, Haiguang Liu, Tie-Yan Liu

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Distributed Stochastic Algorithms for High-Rate Streaming Principal Component Analysis Haroon Raja, Waheed Bajwa

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Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks Lang Liu, Mahdi Milani Fard, Sen Zhao

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Do Better ImageNet Classifiers Assess Perceptual Similarity Better? Manoj Kumar, Neil Houlsby, Nal Kalchbrenner, Ekin Dogus Cubuk

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Do ReLU Networks Have an Edge When Approximating Compactly-Supported Functions? Anastasis Kratsios, Behnoosh Zamanlooy

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Does Entity Abstraction Help Generative Transformers Reason? Nicolas Gontier, Siva Reddy, Christopher Pal

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Domain Invariant Adversarial Learning Matan Levi, Idan Attias, Aryeh Kontorovich

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Domain-Invariant Feature Exploration for Domain Generalization Wang Lu, Jindong Wang, Haoliang Li, Yiqiang Chen, Xing Xie

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DR-DSGD: A Distributionally Robust Decentralized Learning Algorithm over Graphs Chaouki Ben Issaid, Anis Elgabli, Mehdi Bennis

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Efficient CDF Approximations for Normalizing Flows Chandramouli Shama Sastry, Andreas Lehrmann, Marcus A Brubaker, Alexander Radovic

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Efficient Gradient Flows in Sliced-Wasserstein Space Clément Bonet, Nicolas Courty, François Septier, Lucas Drumetz

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Emergent Abilities of Large Language Models Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, William Fedus

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Enhanced Gradient-Based MCMC in Discrete Spaces Benjamin Rhodes, Michael U. Gutmann

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Ensembles of Classifiers: A Bias-Variance Perspective Neha Gupta, Jamie Smith, Ben Adlam, Zelda E Mariet

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Equivariant Mesh Attention Networks Sourya Basu, Jose Gallego-Posada, Francesco Viganò, James Rowbottom, Taco Cohen

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Estimating Potential Outcome Distributions with Collaborating Causal Networks Tianhui Zhou, William E Carson Iv, David Carlson

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Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning Fan Wang, Hao Tian, Haoyi Xiong, Hua Wu, Jie Fu, Yang Cao, Yu Kang, Haifeng Wang

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Explicit Group Sparse Projection with Applications to Deep Learning and NMF Riyasat Ohib, Nicolas Gillis, Niccolo Dalmasso, Sameena Shah, Vamsi K. Potluru, Sergey Plis

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Exploring Efficient Few-Shot Adaptation for Vision Transformers Chengming Xu, Siqian Yang, Yabiao Wang, Zhanxiong Wang, Yanwei Fu, Xiangyang Xue

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Exploring Generative Neural Temporal Point Process Haitao Lin, Lirong Wu, Guojiang Zhao, Liu Pai, Stan Z. Li

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Exploring the Learning Mechanisms of Neural Division Modules Bhumika Mistry, Katayoun Farrahi, Jonathon Hare

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Exposing Outlier Exposure: What Can Be Learned from Few, One, and Zero Outlier Images Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus Robert Muller, Marius Kloft

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Extracting Local Reasoning Chains of Deep Neural Networks Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

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Fail-Safe Adversarial Generative Imitation Learning Philipp Geiger, Christoph-Nikolas Straehle

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Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed Mélanie Bernhardt, Fabio De Sousa Ribeiro, Ben Glocker

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Faking Interpolation Until You Make It Alasdair Paren, Rudra P. K. Poudel, M. Pawan Kumar

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Fast and Accurate Spreading Process Temporal Scale Estimation Abram Magner, Carolyn S Kaminski, Petko Bogdanov

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FedShuffle: Recipes for Better Use of Local Work in Federated Learning Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael Rabbat

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Finding and Fixing Spurious Patterns with Explanations Gregory Plumb, Marco Tulio Ribeiro, Ameet Talwalkar

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Fingerprints of Super Resolution Networks Jeremy Vonderfecht, Feng Liu

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FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data Eugenia Iofinova, Nikola Konstantinov, Christoph H Lampert

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Flipped Classroom: Effective Teaching for Time Series Forecasting Philipp Teutsch, Patrick Mäder

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Fourier Sensitivity and Regularization of Computer Vision Models Kiran Krishnamachari, See-Kiong Ng, Chuan-Sheng Foo

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From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality Fusheng Liu, Haizhao Yang, Soufiane Hayou, Qianxiao Li

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GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ward Ulissi, C. Lawrence Zitnick, Abhishek Das

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Generative Adversarial Neural Operators Md Ashiqur Rahman, Manuel A Florez, Anima Anandkumar, Zachary E Ross, Kamyar Azizzadenesheli

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GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation Haibo Qiu, Baosheng Yu, Dacheng Tao

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GhostSR: Learning Ghost Features for Efficient Image Super-Resolution Ying Nie, Kai Han, Zhenhua Liu, Chuanjian Liu, Yunhe Wang

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GIT: A Generative Image-to-Text Transformer for Vision and Language Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang

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Greedy Bayesian Posterior Approximation with Deep Ensembles Aleksei Tiulpin, Matthew B. Blaschko

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HEAT: Hyperedge Attention Networks Dobrik Georgiev Georgiev, Marc Brockschmidt, Miltiadis Allamanis

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High Fidelity Visualization of What Your Self-Supervised Representation Knows About Florian Bordes, Randall Balestriero, Pascal Vincent

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How Expressive Are Transformers in Spectral Domain for Graphs? Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Hiroki Kanezashi, Toyotaro Suzumura, Isaiah Onando Mulang'

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How to Train Your ViT? Data, Augmentation, and Regularization in Vision Transformers Andreas Peter Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, Lucas Beyer

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Identifiable Deep Generative Models via Sparse Decoding Gemma Elyse Moran, Dhanya Sridhar, Yixin Wang, David Blei

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Identifying Causal Structure in Dynamical Systems Dominik Baumann, Friedrich Solowjow, Karl Henrik Johansson, Sebastian Trimpe

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If Your Data Distribution Shifts, Use Self-Learning Evgenia Rusak, Steffen Schneider, George Pachitariu, Luisa Eck, Peter Vincent Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge

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Improving the Trainability of Deep Neural Networks Through Layerwise Batch-Entropy Regularization David Peer, Bart Keulen, Sebastian Stabinger, Justus Piater, Antonio Rodriguez-sanchez

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Incorporating Sum Constraints into Multitask Gaussian Processes Philipp Pilar, Carl Jidling, Thomas B. Schön, Niklas Wahlström

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Indiscriminate Data Poisoning Attacks on Neural Networks Yiwei Lu, Gautam Kamath, Yaoliang Yu

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Infinitely Wide Limits for Deep Stable Neural Networks: Sub-Linear, Linear and Super-Linear Activation Functions Alberto Bordino, Stefano Favaro, Sandra Fortini

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INR-V: A Continuous Representation Space for Video-Based Generative Tasks Bipasha Sen, Aditya Agarwal, Vinay P Namboodiri, C.V. Jawahar

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Integrating Rankings into Quantized Scores in Peer Review Yusha Liu, Yichong Xu, Nihar B Shah, Aarti Singh

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Interpretable Node Representation with Attribute Decoding Xiaohui Chen, Xi Chen, Liping Liu

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Iterative State Estimation in Non-Linear Dynamical Systems Using Approximate Expectation Propagation Sanket Kamthe, So Takao, Shakir Mohamed, Marc Peter Deisenroth

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Lazy vs Hasty: Linearization in Deep Networks Impacts Learning Schedule Based on Example Difficulty Thomas George, Guillaume Lajoie, Aristide Baratin

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Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent Ajaykrishna Karthikeyan, Naman Jain, Nagarajan Natarajan, Prateek Jain

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Learning Algorithms for Markovian Bandits:\\Is Posterior Sampling More Scalable than Optimism? Nicolas Gast, Bruno Gaujal, Kimang Khun

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Learning the Transformer Kernel Sankalan Pal Chowdhury, Adamos Solomou, Kumar Avinava Dubey, Mrinmaya Sachan

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Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes Vahid Balazadeh, Abir De, Adish Singla, Manuel Gomez Rodriguez

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Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning Tongzhou Mu, Kaixiang Lin, Feiyang Niu, Govind Thattai

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LIMIS: Locally Interpretable Modeling Using Instance-Wise Subsampling Jinsung Yoon, Sercan O Arik, Tomas Pfister

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Linear Algebra with Transformers Francois Charton

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Local Kernel Ridge Regression for Scalable, Interpolating, Continuous Regression Mingxuan Han, Chenglong Ye, Jeff Phillips

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Lookback for Learning to Branch Prateek Gupta, Elias Boutros Khalil, Didier Chételat, Maxime Gasse, Andrea Lodi, Yoshua Bengio, M. Pawan Kumar

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Mace: A Flexible Framework for Membership Privacy Estimation in Generative Models Yixi Xu, Sumit Mukherjee, Xiyang Liu, Shruti Tople, Rahul M Dodhia, Juan M Lavista Ferres

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Max-Affine Spline Insights into Deep Network Pruning Haoran You, Randall Balestriero, Zhihan Lu, Yutong Kou, Huihong Shi, Shunyao Zhang, Shang Wu, Yingyan Lin, Richard Baraniuk

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Mean-Field Langevin Dynamics : Exponential Convergence and Annealing Lénaïc Chizat

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Meta-Learning Sparse Compression Networks Jonathan Schwarz, Yee Whye Teh

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Mitigating Catastrophic Forgetting in Spiking Neural Networks Through Threshold Modulation Ilyass Hammouamri, Timothée Masquelier, Dennis George Wilson

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MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks Ali Ramezani-Kebrya, Iman Tabrizian, Fartash Faghri, Petar Popovski

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Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning Tong Mu, Stephan Zheng, Alexander R Trott

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Modeling Object Dissimilarity for Deep Saliency Prediction Bahar Aydemir, Deblina Bhattacharjee, Tong Zhang, Seungryong Kim, Mathieu Salzmann, Sabine Süsstrunk

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Momentum Capsule Networks Josef Gugglberger, Antonio Rodriguez-sanchez, David Peer

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Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexities Ziyi Chen, Yi Zhou, Rong-Rong Chen

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Multi-Source Causal Inference Using Control Variates Under Outcome Selection Bias Wenshuo Guo, Serena Lutong Wang, Peng Ding, Yixin Wang, Michael Jordan

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Multitask Online Mirror Descent Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice, Massimiliano Pontil

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MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-Based Depth Chenjie Cao, Xinlin Ren, Yanwei Fu

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NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes Suhani Vora, Noha Radwan, Klaus Greff, Henning Meyer, Kyle Genova, Mehdi S. M. Sajjadi, Etienne Pot, Andrea Tagliasacchi, Daniel Duckworth

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No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL Han Wang, Archit Sakhadeo, Adam M White, James M Bell, Vincent Liu, Xutong Zhao, Puer Liu, Tadashi Kozuno, Alona Fyshe, Martha White

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NoiLin: Improving Adversarial Training and Correcting Stereotype of Noisy Labels Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama

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Non-Deterministic Behavior of Thompson Sampling with Linear Payoffs and How to Avoid It Doruk Kilitcioglu, Serdar Kadioglu

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Nonparametric Learning of Two-Layer ReLU Residual Units Zhunxuan Wang, Linyun He, Chunchuan Lyu, Shay B Cohen

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Nonstationary Reinforcement Learning with Linear Function Approximation Huozhi Zhou, Jinglin Chen, Lav R. Varshney, Ashish Jagmohan

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Object-Aware Cropping for Self-Supervised Learning Shlok Kumar Mishra, Anshul Shah, Ankan Bansal, Janit K Anjaria, Abhyuday Narayan Jagannatha, Abhishek Sharma, David Jacobs, Dilip Krishnan

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On Characterizing the Trade-Off in Invariant Representation Learning Bashir Sadeghi, Sepehr Dehdashtian, Vishnu Boddeti

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On Noise Abduction for Answering Counterfactual Queries: A Practical Outlook Saptarshi Saha, Utpal Garain

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On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning Lu Han, Han-Jia Ye, De-Chuan Zhan

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On Robustness to Missing Video for Audiovisual Speech Recognition Oscar Chang, Otavio Braga, Hank Liao, Dmitriy Serdyuk, Olivier Siohan

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On Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks in Besov Spaces Thanh Nguyen-Tang, Sunil Gupta, Hung Tran-The, Svetha Venkatesh

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On the Adversarial Robustness of Vision Transformers Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh

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On the Choice of Interpolation Scheme for Neural CDEs James Morrill, Patrick Kidger, Lingyi Yang, Terry Lyons

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On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons Fangshuo Liao, Anastasios Kyrillidis

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On the Link Between Conscious Function and General Intelligence in Humans and Machines Arthur Juliani, Kai Arulkumaran, Shuntaro Sasai, Ryota Kanai

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On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning Washim Uddin Mondal, Vaneet Aggarwal, Satish Ukkusuri

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On the Origins of the Block Structure Phenomenon in Neural Network Representations Thao Nguyen, Maithra Raghu, Simon Kornblith

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On the Paradox of Certified Training Nikola Jovanović, Mislav Balunovic, Maximilian Baader, Martin Vechev

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On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning Philipp Becker, Gerhard Neumann

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Online Coresets for Parameteric and Non-Parametric Bregman Clustering Supratim Shit, Anirban Dasgupta, Rachit Chhaya, Jayesh Choudhari

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Online Double Oracle Le Cong Dinh, Stephen Marcus McAleer, Zheng Tian, Nicolas Perez-Nieves, Oliver Slumbers, David Henry Mguni, Jun Wang, Haitham Bou Ammar, Yaodong Yang

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Optimal Client Sampling for Federated Learning Wenlin Chen, Samuel Horváth, Peter Richtárik

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Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks David Alvarez-Melis, Yair Schiff, Youssef Mroueh

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Optimizing Intermediate Representations of Generative Models for Phase Retrieval Tobias Uelwer, Sebastian Konietzny, Stefan Harmeling

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Practicality of Generalization Guarantees for Unsupervised Domain Adaptation with Neural Networks Adam Breitholtz, Fredrik Daniel Johansson

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Probabilistic Autoencoder Vanessa M Boehm, Uros Seljak

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QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning Srivatsan Krishnan, Max Lam, Sharad Chitlangia, Zishen Wan, Gabriel Barth-maron, Aleksandra Faust, Vijay Janapa Reddi

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Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, Zhangyang Wang

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Ranking Recovery Under Privacy Considerations Minoh Jeong, Alex Dytso, Martina Cardone

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Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning Baijiong Lin, Feiyang Ye, Yu Zhang, Ivor Tsang

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Recurrent Networks, Hidden States and Beliefs in Partially Observable Environments Gaspard Lambrechts, Adrien Bolland, Damien Ernst

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Reinventing Policy Iteration Under Time Inconsistency Nixie S Lesmana, Huangyuan Su, Chi Seng Pun

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Representation Alignment in Neural Networks Ehsan Imani, Wei Hu, Martha White

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Robust and Data-Efficient Q-Learning by Composite Value-Estimation Gabriel Kalweit, Maria Kalweit, Joschka Boedecker

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Scaling Autoregressive Models for Content-Rich Text-to-Image Generation Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, Yonghui Wu

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Secure Domain Adaptation with Multiple Sources Serban Stan, Mohammad Rostami

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Self-Supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan O Arik, Chen-Yu Lee, Tomas Pfister

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SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, Guodong Long

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Sequentially Learning the Topological Ordering of Directed Acyclic Graphs with Likelihood Ratio Scores Gabriel Ruiz, Oscar Hernan Madrid Padilla, Qing Zhou

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SFP: State-Free Priors for Exploration in Off-Policy Reinforcement Learning Marco Bagatella, Sammy Joe Christen, Otmar Hilliges

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sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification Gabriel Bénédict, Hendrik Vincent Koops, Daan Odijk, Maarten de Rijke

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Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation Zhiqiang Zhong, Sergei Ivanov, Jun Pang

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Sparse Coding with Multi-Layer Decoders Using Variance Regularization Katrina Evtimova, Yann LeCun

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Sparse MoEs Meet Efficient Ensembles James Urquhart Allingham, Florian Wenzel, Zelda E Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton

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Stable and Interpretable Unrolled Dictionary Learning Bahareh Tolooshams, Demba E. Ba

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Stochastic Douglas-Rachford Splitting for Regularized Empirical Risk Minimization: Convergence, Mini-Batch, and Implementation Aysegul Bumin, Kejun Huang

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Structural Learning in Artificial Neural Networks: A Neural Operator Perspective Kaitlin Maile, Luga Hervé, Dennis George Wilson

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Structured Uncertainty in the Observation Space of Variational Autoencoders James Langley, Miguel Monteiro, Charles Jones, Nick Pawlowski, Ben Glocker

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Symbolic Regression Is NP-Hard Marco Virgolin, Solon P Pissis

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Systematically and Efficiently Improving $k$-Means Initialization by Pairwise-Nearest-Neighbor Smoothing Carlo Baldassi

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Teacher’s Pet: Understanding and Mitigating Biases in Distillation Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar

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Teaching Models to Express Their Uncertainty in Words Stephanie Lin, Jacob Hilton, Owain Evans

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The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning Anders Johan Andreassen, Yasaman Bahri, Behnam Neyshabur, Rebecca Roelofs

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The Fundamental Limits of Neural Networks for Interval Certified Robustness Matthew B Mirman, Maximilian Baader, Martin Vechev

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The Graph Cut Kernel for Ranked Data Michelangelo Conserva, Marc Peter Deisenroth, K S Sesh Kumar

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Time Series Alignment with Global Invariances Titouan Vayer, Romain Tavenard, Laetitia Chapel, Rémi Flamary, Nicolas Courty, Yann Soullard

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TITRATED: Learned Human Driving Behavior Without Infractions via Amortized Inference Vasileios Lioutas, Adam Scibior, Frank Wood

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TLDR: Twin Learning for Dimensionality Reduction Yannis Kalantidis, Carlos Eduardo Rosar Kos Lassance, Jon Almazán, Diane Larlus

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Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning Linfeng Liu, Xu Han, Dawei Zhou, Liping Liu

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Uncertainty-Based Active Learning for Reading Comprehension Jing Wang, Jie Shen, Xiaofei Ma, Andrew Arnold

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Understanding AdamW Through Proximal Methods and Scale-Freeness Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky, Francesco Orabona

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Understanding Linearity of Cross-Lingual Word Embedding Mappings Xutan Peng, Mark Stevenson, Chenghua Lin, Chen Li

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Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities Andreas Kirsch, Yarin Gal

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Unimodal Likelihood Models for Ordinal Data Ryoya Yamasaki

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Unsupervised Dense Information Retrieval with Contrastive Learning Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, Edouard Grave

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Unsupervised Learning of Neurosymbolic Encoders Eric Zhan, Jennifer J. Sun, Ann Kennedy, Yisong Yue, Swarat Chaudhuri

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Unsupervised Mismatch Localization in Cross-Modal Sequential Data with Application to Mispronunciations Localization Wei Wei, Hengguan Huang, Xiangming Gu, Hao Wang, Ye Wang

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Unsupervised Network Embedding Beyond Homophily Zhiqiang Zhong, Guadalupe Gonzalez, Daniele Grattarola, Jun Pang

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Using Unsupervised Learning to Detect Broken Symmetries, with Relevance to Searches for Parity Violation in Nature. Christopher Gorham Lester

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Variational Disentanglement for Domain Generalization Yufei Wang, Haoliang Li, Hao Cheng, Bihan Wen, Lap-Pui Chau, Alex Kot

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Weight Expansion: A New Perspective on Dropout and Generalization Gaojie Jin, Xinping Yi, Pengfei Yang, Lijun Zhang, Sven Schewe, Xiaowei Huang

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Your Policy Regularizer Is Secretly an Adversary Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Gregoire Detetang, Markus Kunesch, Shane Legg, Pedro A Ortega

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ZerO Initialization: Initializing Neural Networks with Only Zeros and Ones Jiawei Zhao, Florian Tobias Schaefer, Anima Anandkumar

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Zero-Shot Learning with Common Sense Knowledge Graphs Nihal V. Nayak, Stephen Bach

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