| 2022 | (Non-)Convergence Results for Predictive Coding Networks. Simon Frieder, Thomas Lukasiewicz |
| 2022 | 3D Infomax improves GNNs for Molecular Property Prediction. Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió |
| 2022 | 3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design. Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang |
| 2022 | 3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. Peter Richtárik, Igor Sokolov, Elnur Gasanov, Ilyas Fatkhullin, Zhize Li, Eduard Gorbunov |
| 2022 | A He Bai, Renjie Zheng, Jun-Kun Chen, Mingbo Ma, Xintong Li, Liang Huang |
| 2022 | A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks. Huan Zhang, Shiqi Wang, Kaidi Xu, Yihan Wang, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter |
| 2022 | A Closer Look at Smoothness in Domain Adversarial Training. Harsh Rangwani, Sumukh K. Aithal, Mayank Mishra, Arihant Jain, Venkatesh Babu Radhakrishnan |
| 2022 | A Completely Tuning-Free and Robust Approach to Sparse Precision Matrix Estimation. Chau Tran, Guo Yu |
| 2022 | A Consistent and Efficient Evaluation Strategy for Attribution Methods. Yao Rong, Tobias Leemann, Vadim Borisov, Gjergji Kasneci, Enkelejda Kasneci |
| 2022 | A Context-Integrated Transformer-Based Neural Network for Auction Design. Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil Zaheer, Xiaotie Deng |
| 2022 | A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1. Adil Salim, Lukang Sun, Peter Richtárik |
| 2022 | A Convergent and Dimension-Independent Min-Max Optimization Algorithm. Vijay Keswani, Oren Mangoubi, Sushant Sachdeva, Nisheeth K. Vishnoi |
| 2022 | A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications. Lukas Wolf, Ard Kastrati, Martyna Beata Plomecka, Jie-Ming Li, Dustin Klebe, Alexander Veicht, Roger Wattenhofer, Nicolas Langer |
| 2022 | A Difference Standardization Method for Mutual Transfer Learning. Haoqing Xu, Meng Wang, Beilun Wang |
| 2022 | A Differential Entropy Estimator for Training Neural Networks. Georg Pichler, Pierre Jean A. Colombo, Malik Boudiaf, Günther Koliander, Pablo Piantanida |
| 2022 | A Dynamical System Perspective for Lipschitz Neural Networks. Laurent Meunier, Blaise Delattre, Alexandre Araujo, Alexandre Allauzen |
| 2022 | A Framework for Learning to Request Rich and Contextually Useful Information from Humans. Khanh X. Nguyen, Yonatan Bisk, Hal Daumé III |
| 2022 | A Functional Information Perspective on Model Interpretation. Itai Gat, Nitay Calderon, Roi Reichart, Tamir Hazan |
| 2022 | A General Recipe for Likelihood-free Bayesian Optimization. Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon |
| 2022 | A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines. Weichao Zhou, Wenchao Li |
| 2022 | A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs. Lu Bai, Lixin Cui, Edwin R. Hancock |
| 2022 | A Joint Exponential Mechanism For Differentially Private Top-k. Jennifer Gillenwater, Matthew Joseph, Andres Muñoz Medina, Mónica Ribero Diaz |
| 2022 | A Langevin-like Sampler for Discrete Distributions. Ruqi Zhang, Xingchao Liu, Qiang Liu |
| 2022 | A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving. Luca Carminati, Federico Cacciamani, Marco Ciccone, Nicola Gatti |
| 2022 | A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes. Chengchun Shi, Masatoshi Uehara, Jiawei Huang, Nan Jiang |
| 2022 | A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling. Yiting Cao, Chao Lan |
| 2022 | A Modern Self-Referential Weight Matrix That Learns to Modify Itself. Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber |
| 2022 | A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity. Michinari Momma, Chaosheng Dong, Jia Liu |
| 2022 | A Natural Actor-Critic Framework for Zero-Sum Markov Games. Ahmet Alacaoglu, Luca Viano, Niao He, Volkan Cevher |
| 2022 | A Neural Tangent Kernel Perspective of GANs. Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari |
| 2022 | A New Perspective on the Effects of Spectrum in Graph Neural Networks. Mingqi Yang, Yanming Shen, Rui Li, Heng Qi, Qiang Zhang, Baocai Yin |
| 2022 | A Parametric Class of Approximate Gradient Updates for Policy Optimization. Ramki Gummadi, Saurabh Kumar, Junfeng Wen, Dale Schuurmans |
| 2022 | A Psychological Theory of Explainability. Scott Cheng-Hsin Yang, Tomas Folke, Patrick Shafto |
| 2022 | A Random Matrix Analysis of Data Stream Clustering: Coping With Limited Memory Resources. Hugo Lebeau, Romain Couillet, Florent Chatelain |
| 2022 | A Reduction from Linear Contextual Bandit Lower Bounds to Estimation Lower Bounds. Jiahao He, Jiheng Zhang, Rachel Q. Zhang |
| 2022 | A Regret Minimization Approach to Multi-Agent Control. Udaya Ghai, Udari Madhushani, Naomi Ehrich Leonard, Elad Hazan |
| 2022 | A Resilient Distributed Boosting Algorithm. Yuval Filmus, Idan Mehalel, Shay Moran |
| 2022 | A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions. Daniel Lundström, Tianjian Huang, Meisam Razaviyayn |
| 2022 | A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang |
| 2022 | A Simple Guard for Learned Optimizers. Isabeau Prémont-Schwarz, Jaroslav Vitku, Jan Feyereisl |
| 2022 | A Simple Reward-free Approach to Constrained Reinforcement Learning. Sobhan Miryoosefi, Chi Jin |
| 2022 | A Simple Unified Framework for High Dimensional Bandit Problems. Wenjie Li, Adarsh Barik, Jean Honorio |
| 2022 | A Simple yet Universal Strategy for Online Convex Optimization. Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang |
| 2022 | A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization. Songtao Lu |
| 2022 | A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning. Archit Sharma, Rehaan Ahmad, Chelsea Finn |
| 2022 | A Statistical Manifold Framework for Point Cloud Data. Yonghyeon Lee, Seungyeon Kim, Jinwon Choi, Frank Chongwoo Park |
| 2022 | A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms. Xinwei Zhang, Mingyi Hong, Sairaj V. Dhople, Nicola Elia |
| 2022 | A Study of Face Obfuscation in ImageNet. Kaiyu Yang, Jacqueline H. Yau, Li Fei-Fei, Jia Deng, Olga Russakovsky |
| 2022 | A Study on the Ramanujan Graph Property of Winning Lottery Tickets. Bithika Pal, Arindam Biswas, Sudeshna Kolay, Pabitra Mitra, Biswajit Basu |
| 2022 | A Temporal-Difference Approach to Policy Gradient Estimation. Samuele Tosatto, Andrew Patterson, Martha White, Rupam Mahmood |
| 2022 | A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization. Renzhe Xu, Xingxuan Zhang, Zheyan Shen, Tong Zhang, Peng Cui |
| 2022 | A Theoretical Comparison of Graph Neural Network Extensions. Pál András Papp, Roger Wattenhofer |
| 2022 | A Tighter Analysis of Spectral Clustering, and Beyond. Peter Macgregor, He Sun |
| 2022 | A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources. Xiaoqing Tan, Chung-Chou H. Chang, Ling Zhou, Lu Tang |
| 2022 | A Unified View on PAC-Bayes Bounds for Meta-Learning. Arezou Rezazadeh |
| 2022 | A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks. Yu Pan, Zeyong Su, Ao Liu, Jingquan Wang, Nannan Li, Zenglin Xu |
| 2022 | A data-driven approach for learning to control computers. Peter Conway Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, Timothy P. Lillicrap |
| 2022 | A deep convolutional neural network that is invariant to time rescaling. Brandon G. Jacques, Zoran Tiganj, Aakash Sarkar, Marc W. Howard, Per B. Sederberg |
| 2022 | A new similarity measure for covariate shift with applications to nonparametric regression. Reese Pathak, Cong Ma, Martin J. Wainwright |
| 2022 | A query-optimal algorithm for finding counterfactuals. Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan |
| 2022 | AGNAS: Attention-Guided Micro and Macro-Architecture Search. Zihao Sun, Yu Hu, Shun Lu, Longxing Yang, Jilin Mei, Yinhe Han, Xiaowei Li |
| 2022 | ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD. Karl Bäckström, Marina Papatriantafilou, Philippas Tsigas |
| 2022 | Accelerated Federated Learning with Decoupled Adaptive Optimization. Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu, Ji Liu, Dejing Dou |
| 2022 | Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis. Jungbin Kim, Insoon Yang |
| 2022 | Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization. Karan N. Chadha, Gary Cheng, John C. Duchi |
| 2022 | Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip M. Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson |
| 2022 | Accelerating Shapley Explanation via Contributive Cooperator Selection. Guanchu Wang, Yu-Neng Chuang, Mengnan Du, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, Xia Ben Hu |
| 2022 | Accurate Quantization of Measures via Interacting Particle-based Optimization. Lantian Xu, Anna Korba, Dejan Slepcev |
| 2022 | Achieving Fairness at No Utility Cost via Data Reweighing with Influence. Peizhao Li, Hongfu Liu |
| 2022 | Achieving Minimax Rates in Pool-Based Batch Active Learning. Claudio Gentile, Zhilei Wang, Tong Zhang |
| 2022 | Action-Sufficient State Representation Learning for Control with Structural Constraints. Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang |
| 2022 | Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets. Guy Hacohen, Avihu Dekel, Daphna Weinshall |
| 2022 | Active Multi-Task Representation Learning. Yifang Chen, Kevin Jamieson, Simon S. Du |
| 2022 | Active Nearest Neighbor Regression Through Delaunay Refinement. Alexander Kravberg, Giovanni Luca Marchetti, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny, Danica Kragic |
| 2022 | Active Sampling for Min-Max Fairness. Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang |
| 2022 | Active fairness auditing. Tom Yan, Chicheng Zhang |
| 2022 | ActiveHedge: Hedge meets Active Learning. Bhuvesh Kumar, Jacob D. Abernethy, Venkatesh Saligrama |
| 2022 | Actor-Critic based Improper Reinforcement Learning. Mohammadi Zaki, Avi Mohan, Aditya Gopalan, Shie Mannor |
| 2022 | AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems. Wenzheng Hou, Qianqian Xu, Zhiyong Yang, Shilong Bao, Yuan He, Qingming Huang |
| 2022 | AdaGrad Avoids Saddle Points. Kimon Antonakopoulos, Panayotis Mertikopoulos, Georgios Piliouras, Xiao Wang |
| 2022 | Adapting k-means Algorithms for Outliers. Christoph Grunau, Václav Rozhon |
| 2022 | Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato |
| 2022 | Adapting to Mixing Time in Stochastic Optimization with Markovian Data. Ron Dorfman, Kfir Yehuda Levy |
| 2022 | Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction. Zijian Liu, Ta Duy Nguyen, Alina Ene, Huy L. Nguyen |
| 2022 | Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits. Jiatai Huang, Yan Dai, Longbo Huang |
| 2022 | Adaptive Conformal Predictions for Time Series. Margaux Zaffran, Olivier Féron, Yannig Goude, Julie Josse, Aymeric Dieuleveut |
| 2022 | Adaptive Data Analysis with Correlated Observations. Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer |
| 2022 | Adaptive Gaussian Process Change Point Detection. Edoardo Caldarelli, Philippe Wenk, Stefan Bauer, Andreas Krause |
| 2022 | Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum. Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama |
| 2022 | Adaptive Model Design for Markov Decision Process. Siyu Chen, Donglin Yang, Jiayang Li, Senmiao Wang, Zhuoran Yang, Zhaoran Wang |
| 2022 | Adaptive Random Walk Gradient Descent for Decentralized Optimization. Tao Sun, Dongsheng Li, Bao Wang |
| 2022 | Adaptive Second Order Coresets for Data-efficient Machine Learning. Omead Pooladzandi, David Davini, Baharan Mirzasoleiman |
| 2022 | Additive Gaussian Processes Revisited. Xiaoyu Lu, Alexis Boukouvalas, James Hensman |
| 2022 | Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning. Adam R. Villaflor, Zhe Huang, Swapnil Pande, John M. Dolan, Jeff Schneider |
| 2022 | Adversarial Attack and Defense for Non-Parametric Two-Sample Tests. Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli |
| 2022 | Adversarial Attacks on Gaussian Process Bandits. Eric Han, Jonathan Scarlett |
| 2022 | Adversarial Masking for Self-Supervised Learning. Yuge Shi, N. Siddharth, Philip H. S. Torr, Adam R. Kosiorek |
| 2022 | Adversarial Robustness against Multiple and Single l Francesco Croce, Matthias Hein |
| 2022 | Adversarial Vulnerability of Randomized Ensembles. Hassan Dbouk, Naresh R. Shanbhag |
| 2022 | Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization. Xiaojun Xu, Jacky Y. Zhang, Evelyn Ma, Hyun Ho Son, Sanmi Koyejo, Bo Li |
| 2022 | Adversarially Trained Actor Critic for Offline Reinforcement Learning. Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal |
| 2022 | Adversarially trained neural representations are already as robust as biological neural representations. Chong Guo, Michael J. Lee, Guillaume Leclerc, Joel Dapello, Yug Rao, Aleksander Madry, James J. DiCarlo |
| 2022 | Agnostic Learnability of Halfspaces via Logistic Loss. Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp |
| 2022 | Algorithms for the Communication of Samples. Lucas Theis, Noureldin Y. Ahmed |
| 2022 | Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution. Vihang Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M. Blies, Johannes Brandstetter, José Antonio Arjona-Medina, Sepp Hochreiter |
| 2022 | An Analytical Update Rule for General Policy Optimization. Hepeng Li, Nicholas Clavette, Haibo He |
| 2022 | An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings. Meyer Scetbon, Laurent Meunier, Yaniv Romano |
| 2022 | An Equivalence Between Data Poisoning and Byzantine Gradient Attacks. Sadegh Farhadkhani, Rachid Guerraoui, Lê Nguyên Hoang, Oscar Villemaud |
| 2022 | An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming. Jihwan Jeong, Parth Jaggi, Andrew Butler, Scott Sanner |
| 2022 | An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn. Emmanuel Abbe, Elisabetta Cornacchia, Jan Hazla, Christopher Marquis |
| 2022 | An Intriguing Property of Geophysics Inversion. Yinan Feng, Yinpeng Chen, Shihang Feng, Peng Jin, Zicheng Liu, Youzuo Lin |
| 2022 | An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees. Guillaume Braun, Hemant Tyagi, Christophe Biernacki |
| 2022 | Analysis of Stochastic Processes through Replay Buffers. Shirli Di-Castro Shashua, Shie Mannor, Dotan Di Castro |
| 2022 | Analyzing and Mitigating Interference in Neural Architecture Search. Jin Xu, Xu Tan, Kaitao Song, Renqian Luo, Yichong Leng, Tao Qin, Tie-Yan Liu, Jian Li |
| 2022 | Anarchic Federated Learning. Haibo Yang, Xin Zhang, Prashant Khanduri, Jia Liu |
| 2022 | Antibody-Antigen Docking and Design via Hierarchical Structure Refinement. Wengong Jin, Regina Barzilay, Tommi S. Jaakkola |
| 2022 | Anticorrelated Noise Injection for Improved Generalization. Antonio Orvieto, Hans Kersting, Frank Proske, Francis R. Bach, Aurélien Lucchi |
| 2022 | AnyMorph: Learning Transferable Polices By Inferring Agent Morphology. Brandon Trabucco, Mariano Phielipp, Glen Berseth |
| 2022 | Anytime Information Cascade Popularity Prediction via Self-Exciting Processes. Xi Zhang, Akshay Aravamudan, Georgios C. Anagnostopoulos |
| 2022 | Approximate Bayesian Computation with Domain Expert in the Loop. Ayush Bharti, Louis Filstroff, Samuel Kaski |
| 2022 | Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets. Baojian Zhou, Yifan Sun |
| 2022 | Approximately Equivariant Networks for Imperfectly Symmetric Dynamics. Rui Wang, Robin Walters, Rose Yu |
| 2022 | Architecture Agnostic Federated Learning for Neural Networks. Disha Makhija, Xing Han, Nhat Ho, Joydeep Ghosh |
| 2022 | Asking for Knowledge (AFK): Training RL Agents to Query External Knowledge Using Language. Iou-Jen Liu, Xingdi Yuan, Marc-Alexandre Côté, Pierre-Yves Oudeyer, Alexander G. Schwing |
| 2022 | Asymptotically-Optimal Gaussian Bandits with Side Observations. Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai |
| 2022 | Attentional Meta-learners for Few-shot Polythetic Classification. Ben J. Day, Ramón Viñas Torné, Nikola Simidjievski, Pietro Lió |
| 2022 | Augment with Care: Contrastive Learning for Combinatorial Problems. Haonan Duan, Pashootan Vaezipoor, Max B. Paulus, Yangjun Ruan, Chris J. Maddison |
| 2022 | AutoIP: A United Framework to Integrate Physics into Gaussian Processes. Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney |
| 2022 | AutoSNN: Towards Energy-Efficient Spiking Neural Networks. Byunggook Na, Jisoo Mok, Seongsik Park, Dongjin Lee, Hyeokjun Choe, Sungroh Yoon |
| 2022 | Auxiliary Learning with Joint Task and Data Scheduling. Hong Chen, Xin Wang, Chaoyu Guan, Yue Liu, Wenwu Zhu |
| 2022 | BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression. Zhao Tang Luo, Huiyan Sang, Bani K. Mallick |
| 2022 | BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation. Junnan Li, Dongxu Li, Caiming Xiong, Steven C. H. Hoi |
| 2022 | BabelTower: Learning to Auto-parallelized Program Translation. Yuanbo Wen, Qi Guo, Qiang Fu, Xiaqing Li, Jianxing Xu, Yanlin Tang, Yongwei Zhao, Xing Hu, Zidong Du, Ling Li, Chao Wang, Xuehai Zhou, Yunji Chen |
| 2022 | Balancing Discriminability and Transferability for Source-Free Domain Adaptation. Jogendra Nath Kundu, Akshay R. Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Anand Kulkarni, Varun Jampani, Venkatesh Babu Radhakrishnan |
| 2022 | Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning. Angelo Damiani, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli |
| 2022 | Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity. Vladimir R. Kostic, Saverio Salzo, Massimiliano Pontil |
| 2022 | Batched Dueling Bandits. Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan |
| 2022 | Bayesian Continuous-Time Tucker Decomposition. Shikai Fang, Akil Narayan, Robert M. Kirby, Shandian Zhe |
| 2022 | Bayesian Deep Embedding Topic Meta-Learner. Zhibin Duan, Yishi Xu, Jianqiao Sun, Bo Chen, Wenchao Chen, Chaojie Wang, Mingyuan Zhou |
| 2022 | Bayesian Imitation Learning for End-to-End Mobile Manipulation. Yuqing Du, Daniel Ho, Alex Alemi, Eric Jang, Mohi Khansari |
| 2022 | Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense. Bao Gia Doan, Ehsan Abbasnejad, Javen Qinfeng Shi, Damith C. Ranasinghe |
| 2022 | Bayesian Model Selection, the Marginal Likelihood, and Generalization. Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson |
| 2022 | Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations. Fan Yin, Jieying Jiao, Jun Yan, Guanyu Hu |
| 2022 | Bayesian Nonparametrics for Offline Skill Discovery. Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem |
| 2022 | Bayesian Optimization for Distributionally Robust Chance-constrained Problem. Yu Inatsu, Shion Takeno, Masayuki Karasuyama, Ichiro Takeuchi |
| 2022 | Bayesian Optimization under Stochastic Delayed Feedback. Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low |
| 2022 | Be Like Water: Adaptive Floating Point for Machine Learning. Thomas Y. Yeh, Max Sterner, Zerlina Lai, Brandon Chuang, Alexander Ihler |
| 2022 | Being Properly Improper. Tyler Sypherd, Richard Nock, Lalitha Sankar |
| 2022 | Benchmarking and Analyzing Point Cloud Classification under Corruptions. Jiawei Ren, Liang Pan, Ziwei Liu |
| 2022 | Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint. Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao |
| 2022 | Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features. Zhaowei Zhu, Jialu Wang, Yang Liu |
| 2022 | Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity. Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie |
| 2022 | Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning. Yunhao Tang |
| 2022 | Biological Sequence Design with GFlowNets. Moksh Jain, Emmanuel Bengio, Alex Hernández-García, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio |
| 2022 | Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning. Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine |
| 2022 | Bit Prioritization in Variational Autoencoders via Progressive Coding. Rui Shu, Stefano Ermon |
| 2022 | Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization. Jaehong Yoon, Geon Park, Wonyong Jeong, Sung Ju Hwang |
| 2022 | Black-Box Tuning for Language-Model-as-a-Service. Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, Xipeng Qiu |
| 2022 | Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning. Seyed Kamyar Seyed Ghasemipour, Satoshi Kataoka, Byron David, Daniel Freeman, Shixiang Shane Gu, Igor Mordatch |
| 2022 | Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness. Namuk Park, Songkuk Kim |
| 2022 | Boosting Graph Structure Learning with Dummy Nodes. Xin Liu, Jiayang Cheng, Yangqiu Song, Xin Jiang |
| 2022 | Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization. Giuseppe Bruno De Luca, Eva Silverstein |
| 2022 | Bounding Training Data Reconstruction in Private (Deep) Learning. Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten |
| 2022 | Bounding the Width of Neural Networks via Coupled Initialization A Worst Case Analysis. Alexander Munteanu, Simon Omlor, Zhao Song, David P. Woodruff |
| 2022 | Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding. Yifan Peng, Siddharth Dalmia, Ian R. Lane, Shinji Watanabe |
| 2022 | Branching Reinforcement Learning. Yihan Du, Wei Chen |
| 2022 | Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities. Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, Matthias Hein |
| 2022 | Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits. Avishek Ghosh, Abishek Sankararaman |
| 2022 | Bregman Neural Networks. Jordan Frécon, Gilles Gasso, Massimiliano Pontil, Saverio Salzo |
| 2022 | Bregman Power k-Means for Clustering Exponential Family Data. Adithya Vellal, Saptarshi Chakraborty, Jason Q. Xu |
| 2022 | Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes. Tim Tsz-Kit Lau, Han Liu |
| 2022 | Building Robust Ensembles via Margin Boosting. Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala |
| 2022 | Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning. Cristiano Capone, Cosimo Lupo, Paolo Muratore, Pier Stanislao Paolucci |
| 2022 | ButterflyFlow: Building Invertible Layers with Butterfly Matrices. Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon |
| 2022 | Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums. Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan |
| 2022 | C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra. Yuka Hashimoto, Zhao Wang, Tomoko Matsui |
| 2022 | C-MinHash: Improving Minwise Hashing with Circulant Permutation. Xiaoyun Li, Ping Li |
| 2022 | CITRIS: Causal Identifiability from Temporal Intervened Sequences. Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Stratis Gavves |
| 2022 | COAT: Measuring Object Compositionality in Emergent Representations. Sirui Xie, Ari S. Morcos, Song-Chun Zhu, Ramakrishna Vedantam |
| 2022 | COLA: Consistent Learning with Opponent-Learning Awareness. Timon Willi, Alistair Letcher, Johannes Treutlein, Jakob N. Foerster |
| 2022 | Calibrated Learning to Defer with One-vs-All Classifiers. Rajeev Verma, Eric T. Nalisnick |
| 2022 | Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation. Volodymyr Kuleshov, Shachi Deshpande |
| 2022 | Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning. Yingjie Fei, Ruitu Xu |
| 2022 | Causal Conceptions of Fairness and their Consequences. Hamed Nilforoshan, Johann D. Gaebler, Ravi Shroff, Sharad Goel |
| 2022 | Causal Dynamics Learning for Task-Independent State Abstraction. Zizhao Wang, Xuesu Xiao, Zifan Xu, Yuke Zhu, Peter Stone |
| 2022 | Causal Imitation Learning under Temporally Correlated Noise. Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu |
| 2022 | Causal Inference Through the Structural Causal Marginal Problem. Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing |
| 2022 | Causal Transformer for Estimating Counterfactual Outcomes. Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel |
| 2022 | Causal structure-based root cause analysis of outliers. Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum, Dominik Janzing |
| 2022 | Centroid Approximation for Bootstrap: Improving Particle Quality at Inference. Mao Ye, Qiang Liu |
| 2022 | CerDEQ: Certifiable Deep Equilibrium Model. Mingjie Li, Yisen Wang, Zhouchen Lin |
| 2022 | Certified Adversarial Robustness Under the Bounded Support Set. Yiwen Kou, Qinyuan Zheng, Yisen Wang |
| 2022 | Certified Neural Network Watermarks with Randomized Smoothing. Arpit Bansal, Ping-Yeh Chiang, Michael J. Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P. Dickerson, Tom Goldstein |
| 2022 | Certified Robustness Against Natural Language Attacks by Causal Intervention. Haiteng Zhao, Chang Ma, Xinshuai Dong, Anh Tuan Luu, Zhi-Hong Deng, Hanwang Zhang |
| 2022 | Certifying Out-of-Domain Generalization for Blackbox Functions. Maurice Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, Ce Zhang |
| 2022 | Channel Importance Matters in Few-Shot Image Classification. Xu Luo, Jing Xu, Zenglin Xu |
| 2022 | Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks. Nan Wu, Stanislaw Jastrzebski, Kyunghyun Cho, Krzysztof J. Geras |
| 2022 | Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits. Marc Jourdan, Rémy Degenne |
| 2022 | Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding. Lan-Zhe Guo, Yufeng Li |
| 2022 | Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments. Ryan Sullivan, J. K. Terry, Benjamin Black, John P. Dickerson |
| 2022 | Closed-Form Diffeomorphic Transformations for Time Series Alignment. Iñigo Martinez, Elisabeth Viles, Igor G. Olaizola |
| 2022 | Co-training Improves Prompt-based Learning for Large Language Models. Hunter Lang, Monica N. Agrawal, Yoon Kim, David A. Sontag |
| 2022 | Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets. Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wang, Zhangyang Wang |
| 2022 | Coin Flipping Neural Networks. Yuval Sieradzki, Nitzan Hodos, Gal Yehuda, Assaf Schuster |
| 2022 | Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs. Yikang Zhang, Zhuo Chen, Zhao Zhong |
| 2022 | Combining Diverse Feature Priors. Saachi Jain, Dimitris Tsipras, Aleksander Madry |
| 2022 | Communicating via Markov Decision Processes. Samuel Sokota, Christian A. Schröder de Witt, Maximilian Igl, Luisa M. Zintgraf, Philip H. S. Torr, Martin Strohmeier, J. Zico Kolter, Shimon Whiteson, Jakob N. Foerster |
| 2022 | Communication-Efficient Adaptive Federated Learning. Yujia Wang, Lu Lin, Jinghui Chen |
| 2022 | Communication-efficient Distributed Learning for Large Batch Optimization. Rui Liu, Barzan Mozafari |
| 2022 | Composing Partial Differential Equations with Physics-Aware Neural Networks. Matthias Karlbauer, Timothy Praditia, Sebastian Otte, Sergey Oladyshkin, Wolfgang Nowak, Martin V. Butz |
| 2022 | Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning. Hidetaka Kamigaito, Katsuhiko Hayashi |
| 2022 | Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data. Timothy J. Castiglia, Anirban Das, Shiqiang Wang, Stacy Patterson |
| 2022 | Conditional GANs with Auxiliary Discriminative Classifier. Liang Hou, Qi Cao, Huawei Shen, Siyuan Pan, Xiaoshuang Li, Xueqi Cheng |
| 2022 | Confidence Score for Source-Free Unsupervised Domain Adaptation. Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon |
| 2022 | Conformal Prediction Sets with Limited False Positives. Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay |
| 2022 | Congested Bandits: Optimal Routing via Short-term Resets. Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias |
| 2022 | Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation. Kendrick Shen, Robbie M. Jones, Ananya Kumar, Sang Michael Xie, Jeff Z. HaoChen, Tengyu Ma, Percy Liang |
| 2022 | Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures. Nelson Vadori, Rahul Savani, Thomas Spooner, Sumitra Ganesh |
| 2022 | Consistent Polyhedral Surrogates for Top-k Classification and Variants. Anish Thilagar, Rafael M. Frongillo, Jessica Finocchiaro, Emma Goodwill |
| 2022 | Constants Matter: The Performance Gains of Active Learning. Stephen O. Mussmann, Sanjoy Dasgupta |
| 2022 | Constrained Discrete Black-Box Optimization using Mixed-Integer Programming. Theodore P. Papalexopoulos, Christian Tjandraatmadja, Ross Anderson, Juan Pablo Vielma, David Belanger |
| 2022 | Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks. Weiran Lin, Keane Lucas, Lujo Bauer, Michael K. Reiter, Mahmood Sharif |
| 2022 | Constrained Offline Policy Optimization. Nicholas Polosky, Bruno C. da Silva, Madalina Fiterau, Jithin Jagannath |
| 2022 | Constrained Optimization with Dynamic Bound-scaling for Effective NLP Backdoor Defense. Guangyu Shen, Yingqi Liu, Guanhong Tao, Qiuling Xu, Zhuo Zhang, Shengwei An, Shiqing Ma, Xiangyu Zhang |
| 2022 | Constrained Variational Policy Optimization for Safe Reinforcement Learning. Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao |
| 2022 | Constraint-based graph network simulator. Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Peter W. Battaglia |
| 2022 | Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold. Sugandha Sharma, Sarthak Chandra, Ila R. Fiete |
| 2022 | ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers. Kaizhi Qian, Yang Zhang, Heting Gao, Junrui Ni, Cheng-I Lai, David D. Cox, Mark Hasegawa-Johnson, Shiyu Chang |
| 2022 | Context-Aware Drift Detection. Oliver Cobb, Arnaud Van Looveren |
| 2022 | Contextual Bandits with Large Action Spaces: Made Practical. Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro |
| 2022 | Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces. Yinglun Zhu, Paul Mineiro |
| 2022 | Contextual Information-Directed Sampling. Botao Hao, Tor Lattimore, Chao Qin |
| 2022 | Continual Learning via Sequential Function-Space Variational Inference. Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal |
| 2022 | Continual Learning with Guarantees via Weight Interval Constraints. Maciej Wolczyk, Karol J. Piczak, Bartosz Wójcik, Lukasz Pustelnik, Pawel Morawiecki, Jacek Tabor, Tomasz Trzcinski, Przemyslaw Spurek |
| 2022 | Continual Repeated Annealed Flow Transport Monte Carlo. Alexander G. de G. Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet |
| 2022 | Continuous Control with Action Quantization from Demonstrations. Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin |
| 2022 | Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems. Jaewook J. Suh, Gyumin Roh, Ernest K. Ryu |
| 2022 | Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations. Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar |
| 2022 | Contrastive Learning with Boosted Memorization. Zhihan Zhou, Jiangchao Yao, Yanfeng Wang, Bo Han, Ya Zhang |
| 2022 | Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness. Adam Foster, Árpi Vezér, Craig A. Glastonbury, Páidí Creed, Samer Abujudeh, Aaron Sim |
| 2022 | Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning. Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang |
| 2022 | Controlling Conditional Language Models without Catastrophic Forgetting. Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman |
| 2022 | Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness. Kevin Scaman, Cédric Malherbe, Ludovic Dos Santos |
| 2022 | Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering. Peng Wang, Huikang Liu, Anthony Man-Cho So, Laura Balzano |
| 2022 | Convergence of Invariant Graph Networks. Chen Cai, Yusu Wang |
| 2022 | Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime. James-Michael Leahy, Bekzhan Kerimkulov, David Siska, Lukasz Szpruch |
| 2022 | Convergence of Uncertainty Sampling for Active Learning. Anant Raj, Francis R. Bach |
| 2022 | Convolutional and Residual Networks Provably Contain Lottery Tickets. Rebekka Burkholz |
| 2022 | Cooperative Online Learning in Stochastic and Adversarial MDPs. Tal Lancewicki, Aviv Rosenberg, Yishay Mansour |
| 2022 | Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms. Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor |
| 2022 | Coordinated Double Machine Learning. Nitai Fingerhut, Matteo Sesia, Yaniv Romano |
| 2022 | Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations. Michael Zhang, Nimit Sharad Sohoni, Hongyang R. Zhang, Chelsea Finn, Christopher Ré |
| 2022 | Correlated Quantization for Distributed Mean Estimation and Optimization. Ananda Theertha Suresh, Ziteng Sun, Jae Ro, Felix X. Yu |
| 2022 | Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds. Nate Veldt |
| 2022 | Counterfactual Prediction for Outcome-Oriented Treatments. Hao Zou, Bo Li, Jiangang Han, Shuiping Chen, Xuetao Ding, Peng Cui |
| 2022 | Counterfactual Transportability: A Formal Approach. Juan D. Correa, Sanghack Lee, Elias Bareinboim |
| 2022 | Cross-Space Active Learning on Graph Convolutional Networks. Yufei Tao, Hao Wu, Shiyuan Deng |
| 2022 | CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer. Yao Mark Mu, Shoufa Chen, Mingyu Ding, Jianyu Chen, Runjian Chen, Ping Luo |
| 2022 | Curriculum Reinforcement Learning via Constrained Optimal Transport. Pascal Klink, Haoyi Yang, Carlo D'Eramo, Jan Peters, Joni Pajarinen |
| 2022 | Cycle Representation Learning for Inductive Relation Prediction. Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen |
| 2022 | DAVINZ: Data Valuation using Deep Neural Networks at Initialization. Zhaoxuan Wu, Yao Shu, Bryan Kian Hsiang Low |
| 2022 | DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning. Robert Hönig, Yiren Zhao, Robert Mullins |
| 2022 | DNA: Domain Generalization with Diversified Neural Averaging. Xu Chu, Yujie Jin, Wenwu Zhu, Yasha Wang, Xin Wang, Shanghang Zhang, Hong Mei |
| 2022 | DNNR: Differential Nearest Neighbors Regression. Youssef Nader, Leon Sixt, Tim Landgraf |
| 2022 | DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning. Hassam Sheikh, Kizza Frisbee, Mariano Phielipp |
| 2022 | DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks. Zhuang Wang, Zhaozhuo Xu, Xinyu Crystal Wu, Anshumali Shrivastava, T. S. Eugene Ng |
| 2022 | DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck. Jiameng Fan, Wenchao Li |
| 2022 | DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting. Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, Pyang Li |
| 2022 | Data Augmentation as Feature Manipulation. Ruoqi Shen, Sébastien Bubeck, Suriya Gunasekar |
| 2022 | Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP). Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt |
| 2022 | Data Scaling Laws in NMT: The Effect of Noise and Architecture. Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, Orhan Firat |
| 2022 | Data-Efficient Double-Win Lottery Tickets from Robust Pre-training. Tianlong Chen, Zhenyu Zhang, Sijia Liu, Yang Zhang, Shiyu Chang, Zhangyang Wang |
| 2022 | Data-SUITE: Data-centric identification of in-distribution incongruous examples. Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar |
| 2022 | Datamodels: Understanding Predictions with Data and Data with Predictions. Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry |
| 2022 | Dataset Condensation via Efficient Synthetic-Data Parameterization. Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, Hyun Oh Song |
| 2022 | Dataset Condensation with Contrastive Signals. Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon |
| 2022 | De novo mass spectrometry peptide sequencing with a transformer model. Melih Yilmaz, William Fondrie, Wout Bittremieux, Sewoong Oh, William S. Noble |
| 2022 | Debiaser Beware: Pitfalls of Centering Regularized Transport Maps. Aram-Alexandre Pooladian, Marco Cuturi, Jonathan Niles-Weed |
| 2022 | Decentralized Online Convex Optimization in Networked Systems. Yiheng Lin, Judy Gan, Guannan Qu, Yash Kanoria, Adam Wierman |
| 2022 | Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm. Malik Tiomoko, Ekkehard Schnoor, Mohamed El Amine Seddik, Igor Colin, Aladin Virmaux |
| 2022 | Decision-Focused Learning: Through the Lens of Learning to Rank. Jayanta Mandi, Víctor Bucarey, Maxime Mulamba Ke Tchomba, Tias Guns |
| 2022 | Decomposing Temporal High-Order Interactions via Latent ODEs. Shibo Li, Robert M. Kirby, Shandian Zhe |
| 2022 | Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning. Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Changjie Fan, Fei Wu, Jun Xiao |
| 2022 | Deduplicating Training Data Mitigates Privacy Risks in Language Models. Nikhil Kandpal, Eric Wallace, Colin Raffel |
| 2022 | Deep Causal Metric Learning. Xiang Deng, Zhongfei Zhang |
| 2022 | Deep Hierarchy in Bandits. Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh |
| 2022 | Deep Network Approximation in Terms of Intrinsic Parameters. Zuowei Shen, Haizhao Yang, Shijun Zhang |
| 2022 | Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry. Fabrizio Pittorino, Antonio Ferraro, Gabriele Perugini, Christoph Feinauer, Carlo Baldassi, Riccardo Zecchina |
| 2022 | Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning. Chang Liu, Chenfei Lou, Runzhong Wang, Alan Yuhan Xi, Li Shen, Junchi Yan |
| 2022 | Deep Probability Estimation. Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda |
| 2022 | Deep Reference Priors: What is the best way to pretrain a model? Yansong Gao, Rahul Ramesh, Pratik Chaudhari |
| 2022 | Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm. Huayi Tang, Yong Liu |
| 2022 | Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage. Alan J. X. Guo, Cong Liang, Qing-Hu Hou |
| 2022 | Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection. Wenchao Chen, Long Tian, Bo Chen, Liang Dai, Zhibin Duan, Mingyuan Zhou |
| 2022 | Deep and Flexible Graph Neural Architecture Search. Wentao Zhang, Zheyu Lin, Yu Shen, Yang Li, Zhi Yang, Bin Cui |
| 2022 | Deep equilibrium networks are sensitive to initialization statistics. Atish Agarwala, Samuel S. Schoenholz |
| 2022 | Deep symbolic regression for recurrence prediction. Stéphane d'Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, François Charton |
| 2022 | DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale. Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He |
| 2022 | Delay-Adaptive Step-sizes for Asynchronous Learning. Xuyang Wu, Sindri Magnússon, Hamid Reza Feyzmahdavian, Mikael Johansson |
| 2022 | Delayed Reinforcement Learning by Imitation. Pierre Liotet, Davide Maran, Lorenzo Bisi, Marcello Restelli |
| 2022 | Deletion Robust Submodular Maximization over Matroids. Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam |
| 2022 | Demystifying the Adversarial Robustness of Random Transformation Defenses. Chawin Sitawarin, Zachary J. Golan-Strieb, David A. Wagner |
| 2022 | Denoised MDPs: Learning World Models Better Than the World Itself. Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian |
| 2022 | Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations. Mohammad Mahmudul Alam, Edward Raff, Tim Oates, James Holt |
| 2022 | DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks. Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan Lin |
| 2022 | Describing Differences between Text Distributions with Natural Language. Ruiqi Zhong, Charlie Snell, Dan Klein, Jacob Steinhardt |
| 2022 | Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization. Brandon Trabucco, Xinyang Geng, Aviral Kumar, Sergey Levine |
| 2022 | Detached Error Feedback for Distributed SGD with Random Sparsification. An Xu, Heng Huang |
| 2022 | Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them. Florian Tramèr |
| 2022 | Detecting Corrupted Labels Without Training a Model to Predict. Zhaowei Zhu, Zihao Dong, Yang Liu |
| 2022 | Dialog Inpainting: Turning Documents into Dialogs. Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Y. Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, Kelvin Guu |
| 2022 | Difference Advantage Estimation for Multi-Agent Policy Gradients. Yueheng Li, Guangming Xie, Zongqing Lu |
| 2022 | Differentiable Top-k Classification Learning. Felix Petersen, Hilde Kuehne, Christian Borgelt, Oliver Deussen |
| 2022 | Differentially Private Approximate Quantiles. Haim Kaplan, Shachar Schnapp, Uri Stemmer |
| 2022 | Differentially Private Community Detection for Stochastic Block Models. Mohamed S. Mohamed, Dung Nguyen, Anil Vullikanti, Ravi Tandon |
| 2022 | Differentially Private Coordinate Descent for Composite Empirical Risk Minimization. Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi |
| 2022 | Differentially Private Maximal Information Coefficients. John Lazarsfeld, Aaron Johnson, Emmanuel Adéníran |
| 2022 | Diffusion Models for Adversarial Purification. Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Animashree Anandkumar |
| 2022 | Diffusion bridges vector quantized variational autoencoders. Max Cohen, Guillaume Quispe, Sylvain Le Corff, Charles Ollion, Eric Moulines |
| 2022 | Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization. Dongruo Zhou, Quanquan Gu |
| 2022 | Direct Behavior Specification via Constrained Reinforcement Learning. Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher J. Pal |
| 2022 | Directed Acyclic Transformer for Non-Autoregressive Machine Translation. Fei Huang, Hao Zhou, Yang Liu, Hang Li, Minlie Huang |
| 2022 | DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training. Rong Dai, Li Shen, Fengxiang He, Xinmei Tian, Dacheng Tao |
| 2022 | Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning. Aviv Netanyahu, Tianmin Shu, Joshua B. Tenenbaum, Pulkit Agrawal |
| 2022 | Discrete Probabilistic Inverse Optimal Transport. Wei-Ting Chiu, Pei Wang, Patrick Shafto |
| 2022 | Discrete Tree Flows via Tree-Structured Permutations. Mai Elkady, Hyung Zin Lim, David I. Inouye |
| 2022 | Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations. Haoran Xu, Xianyuan Zhan, Honglei Yin, Huiling Qin |
| 2022 | Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring. Zhengquan Luo, Yunlong Wang, Zilei Wang, Zhenan Sun, Tieniu Tan |
| 2022 | Disentangling Disease-related Representation from Obscure for Disease Prediction. Chu-ran Wang, Fei Gao, Fandong Zhang, Fangwei Zhong, Yizhou Yu, Yizhou Wang |
| 2022 | Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning. Kyunghwan Son, Junsu Kim, Sungsoo Ahn, Roben Delos Reyes, Yung Yi, Jinwoo Shin |
| 2022 | Distinguishing rule and exemplar-based generalization in learning systems. Ishita Dasgupta, Erin Grant, Tom Griffiths |
| 2022 | Distribution Regression with Sliced Wasserstein Kernels. Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto |
| 2022 | Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning. Harley E. Wiltzer, David Meger, Marc G. Bellemare |
| 2022 | Distributionally Robust Q-Learning. Zijian Liu, Qinxun Bai, Jose H. Blanchet, Perry Dong, Wei Xu, Zhengqing Zhou, Zhengyuan Zhou |
| 2022 | Distributionally-Aware Kernelized Bandit Problems for Risk Aversion. Sho Takemori |
| 2022 | Divergence-Regularized Multi-Agent Actor-Critic. Kefan Su, Zongqing Lu |
| 2022 | Diversified Adversarial Attacks based on Conjugate Gradient Method. Keiichiro Yamamura, Haruki Sato, Nariaki Tateiwa, Nozomi Hata, Toru Mitsutake, Issa Oe, Hiroki Ishikura, Katsuki Fujisawa |
| 2022 | Do Differentiable Simulators Give Better Policy Gradients? Hyung Ju Terry Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake |
| 2022 | Do More Negative Samples Necessarily Hurt In Contrastive Learning? Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath |
| 2022 | Does the Data Induce Capacity Control in Deep Learning? Rubing Yang, Jialin Mao, Pratik Chaudhari |
| 2022 | Domain Adaptation for Time Series Forecasting via Attention Sharing. Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang |
| 2022 | Double Sampling Randomized Smoothing. Linyi Li, Jiawei Zhang, Tao Xie, Bo Li |
| 2022 | Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning. Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou |
| 2022 | DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations. Fei Deng, Ingook Jang, Sungjin Ahn |
| 2022 | Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images. Tom Ron, Michal Weiler-Sagie, Tamir Hazan |
| 2022 | Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification. Jun-Yi Hang, Min-Ling Zhang |
| 2022 | DynaMixer: A Vision MLP Architecture with Dynamic Mixing. Ziyu Wang, Wenhao Jiang, Yiming Zhu, Li Yuan, Yibing Song, Wei Liu |
| 2022 | Dynamic Regret of Online Markov Decision Processes. Peng Zhao, Longfei Li, Zhi-Hua Zhou |
| 2022 | Dynamic Topic Models for Temporal Document Networks. Delvin Ce Zhang, Hady W. Lauw |
| 2022 | EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning. Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang |
| 2022 | EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning. Shay Vargaftik, Ran Ben Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, Michael Mitzenmacher |
| 2022 | Easy Variational Inference for Categorical Models via an Independent Binary Approximation. Michael T. Wojnowicz, Shuchin Aeron, Eric L. Miller, Michael C. Hughes |
| 2022 | Efficient Approximate Inference for Stationary Kernel on Frequency Domain. Yohan Jung, Kyungwoo Song, Jinkyoo Park |
| 2022 | Efficient Computation of Higher-Order Subgraph Attribution via Message Passing. Ping Xiong, Thomas Schnake, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima |
| 2022 | Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity. Sebastian Shenghong Tay, Chuan Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low |
| 2022 | Efficient Learning for AlphaZero via Path Consistency. Dengwei Zhao, Shikui Tu, Lei Xu |
| 2022 | Efficient Learning of CNNs using Patch Based Features. Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Shwartz |
| 2022 | Efficient Low Rank Convex Bounds for Pairwise Discrete Graphical Models. Valentin Durante, George Katsirelos, Thomas Schiex |
| 2022 | Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation. Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause |
| 2022 | Efficient Online ML API Selection for Multi-Label Classification Tasks. Lingjiao Chen, Matei Zaharia, James Zou |
| 2022 | Efficient PAC Learning from the Crowd with Pairwise Comparisons. Shiwei Zeng, Jie Shen |
| 2022 | Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun |
| 2022 | Efficient Representation Learning via Adaptive Context Pooling. Chen Huang, Walter Talbott, Navdeep Jaitly, Joshua M. Susskind |
| 2022 | Efficient Test-Time Model Adaptation without Forgetting. Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, Mingkui Tan |
| 2022 | Efficient Variance Reduction for Meta-learning. Hansi Yang, James T. Kwok |
| 2022 | Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries. Daniel J. Rosenkrantz, Abhijin Adiga, Madhav V. Marathe, Zirou Qiu, S. S. Ravi, Richard Edwin Stearns, Anil Vullikanti |
| 2022 | End-to-End Balancing for Causal Continuous Treatment-Effect Estimation. Mohammad Taha Bahadori, Eric Tchetgen Tchetgen, David Heckerman |
| 2022 | Entropic Causal Inference: Graph Identifiability. Spencer Compton, Kristjan H. Greenewald, Dmitriy Katz, Murat Kocaoglu |
| 2022 | Entropic Gromov-Wasserstein between Gaussian Distributions. Khang Le, Dung Q. Le, Huy Nguyen, Dat Do, Tung Pham, Nhat Ho |
| 2022 | EqR: Equivariant Representations for Data-Efficient Reinforcement Learning. Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, Siamak Ravanbakhsh |
| 2022 | EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction. Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi S. Jaakkola |
| 2022 | Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent. Weiming Liu, Huacong Jiang, Bin Li, Houqiang Li |
| 2022 | Equivariance versus Augmentation for Spherical Images. Jan E. Gerken, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson |
| 2022 | Equivariant Diffusion for Molecule Generation in 3D. Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, Max Welling |
| 2022 | Equivariant Priors for compressed sensing with unknown orientation. Anna Kuzina, Kumar Pratik, Fabio Valerio Massoli, Arash Behboodi |
| 2022 | Equivariant Quantum Graph Circuits. Péter Mernyei, Konstantinos Meichanetzidis, Ismail Ilkan Ceylan |
| 2022 | Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. Giorgia Dellaferrera, Gabriel Kreiman |
| 2022 | Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network. Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu |
| 2022 | Estimating and Penalizing Induced Preference Shifts in Recommender Systems. Micah D. Carroll, Anca D. Dragan, Stuart Russell, Dylan Hadfield-Menell |
| 2022 | Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models. Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang |
| 2022 | Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing. Ramji Venkataramanan, Kevin Kögler, Marco Mondelli |
| 2022 | Evaluating the Adversarial Robustness of Adaptive Test-time Defenses. Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, A. Taylan Cemgil |
| 2022 | Evolving Curricula with Regret-Based Environment Design. Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel |
| 2022 | Exact Learning of Preference Structure: Single-peaked Preferences and Beyond. Sonja Kraiczy, Edith Elkind |
| 2022 | Exact Optimal Accelerated Complexity for Fixed-Point Iterations. Jisun Park, Ernest K. Ryu |
| 2022 | Examining Scaling and Transfer of Language Model Architectures for Machine Translation. Biao Zhang, Behrooz Ghorbani, Ankur Bapna, Yong Cheng, Xavier Garcia, Jonathan Shen, Orhan Firat |
| 2022 | Exploiting Independent Instruments: Identification and Distribution Generalization. Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister, Jonas Peters |
| 2022 | Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups. David M. Knigge, David W. Romero, Erik J. Bekkers |
| 2022 | Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling. Yuanbang Liang, Jing Wu, Yu-Kun Lai, Yipeng Qin |
| 2022 | Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning. Bobby He, Mete Ozay |
| 2022 | Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control. Liang Zhang, Qiang Wu, Jun Shen, Linyuan Lü, Bo Du, Jianqing Wu |
| 2022 | Extended Unconstrained Features Model for Exploring Deep Neural Collapse. Tom Tirer, Joan Bruna |
| 2022 | Extracting Latent State Representations with Linear Dynamics from Rich Observations. Abraham Frandsen, Rong Ge, Holden Lee |
| 2022 | FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin |
| 2022 | FITNESS: (Fine Tune on New and Similar Samples) to detect anomalies in streams with drift and outliers. Abishek Sankararaman, Balakrishnan Narayanaswamy, Vikramank Y. Singh, Zhao Song |
| 2022 | FOCUS: Familiar Objects in Common and Uncommon Settings. Priyatham Kattakinda, Soheil Feizi |
| 2022 | Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data. Umberto M. Tomasini, Antonio Sclocchi, Matthieu Wyart |
| 2022 | Fair Generalized Linear Models with a Convex Penalty. Hyungrok Do, Preston Putzel, Axel S. Martin, Padhraic Smyth, Judy Zhong |
| 2022 | Fair Representation Learning through Implicit Path Alignment. Changjian Shui, Qi Chen, Jiaqi Li, Boyu Wang, Christian Gagné |
| 2022 | Fair and Fast k-Center Clustering for Data Summarization. Haris Angelidakis, Adam Kurpisz, Leon Sering, Rico Zenklusen |
| 2022 | Fairness Interventions as (Dis)Incentives for Strategic Manipulation. Xueru Zhang, Mohammad Mahdi Khalili, Kun Jin, Parinaz Naghizadeh, Mingyan Liu |
| 2022 | Fairness with Adaptive Weights. Junyi Chai, Xiaoqian Wang |
| 2022 | Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models. Elvis Nava, John Z. Zhang, Mike Yan Michelis, Tao Du, Pingchuan Ma, Benjamin F. Grewe, Wojciech Matusik, Robert Kevin Katzschmann |
| 2022 | Fast Composite Optimization and Statistical Recovery in Federated Learning. Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu |
| 2022 | Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions. Aaron Mishkin, Arda Sahiner, Mert Pilanci |
| 2022 | Fast Finite Width Neural Tangent Kernel. Roman Novak, Jascha Sohl-Dickstein, Samuel S. Schoenholz |
| 2022 | Fast Lossless Neural Compression with Integer-Only Discrete Flows. Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang |
| 2022 | Fast Population-Based Reinforcement Learning on a Single Machine. Arthur Flajolet, Claire Bizon Monroc, Karim Beguir, Thomas Pierrot |
| 2022 | Fast Provably Robust Decision Trees and Boosting. Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou |
| 2022 | Fast Relative Entropy Coding with A* coding. Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato |
| 2022 | Fast and Provable Nonconvex Tensor RPCA. Haiquan Qiu, Yao Wang, Shaojie Tang, Deyu Meng, Quanming Yao |
| 2022 | Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng |
| 2022 | Fast rates for noisy interpolation require rethinking the effect of inductive bias. Konstantin Donhauser, Nicolò Ruggeri, Stefan Stojanovic, Fanny Yang |
| 2022 | Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning. Jiechao Guan, Zhiwu Lu |
| 2022 | Faster Algorithms for Learning Convex Functions. Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly L. Geyer, Venkatesh Saligrama, Brian Kulis |
| 2022 | Faster Fundamental Graph Algorithms via Learned Predictions. Justin Y. Chen, Sandeep Silwal, Ali Vakilian, Fred Zhang |
| 2022 | Faster Privacy Accounting via Evolving Discretization. Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi |
| 2022 | Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. Feynman T. Liang, Michael W. Mahoney, Liam Hodgkinson |
| 2022 | Feature Learning and Signal Propagation in Deep Neural Networks. Yizhang Lou, Chris E. Mingard, Soufiane Hayou |
| 2022 | Feature Space Particle Inference for Neural Network Ensembles. Shingo Yashima, Teppei Suzuki, Kohta Ishikawa, Ikuro Sato, Rei Kawakami |
| 2022 | Feature and Parameter Selection in Stochastic Linear Bandits. Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori, Mahnoosh Alizadeh, Mohammad Ghavamzadeh |
| 2022 | Feature selection using e-values. Subhabrata Majumdar, Snigdhansu Chatterjee |
| 2022 | FedNL: Making Newton-Type Methods Applicable to Federated Learning. Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtárik |
| 2022 | FedNest: Federated Bilevel, Minimax, and Compositional Optimization. Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis, Samet Oymak |
| 2022 | FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning. Anis Elgabli, Chaouki Ben Issaid, Amrit Singh Bedi, Ketan Rajawat, Mehdi Bennis, Vaneet Aggarwal |
| 2022 | FedScale: Benchmarking Model and System Performance of Federated Learning at Scale. Fan Lai, Yinwei Dai, Sanjay Sri Vallabh Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha V. Madhyastha, Mosharaf Chowdhury |
| 2022 | Federated Learning with Label Distribution Skew via Logits Calibration. Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Chao Wu |
| 2022 | Federated Learning with Partial Model Personalization. Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael G. Rabbat, Maziar Sanjabi, Lin Xiao |
| 2022 | Federated Learning with Positive and Unlabeled Data. Xinyang Lin, Hanting Chen, Yixing Xu, Chao Xu, Xiaolin Gui, Yiping Deng, Yunhe Wang |
| 2022 | Federated Minimax Optimization: Improved Convergence Analyses and Algorithms. Pranay Sharma, Rohan Panda, Gauri Joshi, Pramod K. Varshney |
| 2022 | Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling. Sajad Khodadadian, Pranay Sharma, Gauri Joshi, Siva Theja Maguluri |
| 2022 | Fenrir: Physics-Enhanced Regression for Initial Value Problems. Filip Tronarp, Nathanael Bosch, Philipp Hennig |
| 2022 | Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games. Lucas Baudin, Rida Laraki |
| 2022 | Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming. Chuan Wen, Jianing Qian, Jierui Lin, Jiaye Teng, Dinesh Jayaraman, Yang Gao |
| 2022 | Finding Global Homophily in Graph Neural Networks When Meeting Heterophily. Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian |
| 2022 | Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks. Runpei Dong, Zhanhong Tan, Mengdi Wu, Linfeng Zhang, Kaisheng Ma |
| 2022 | Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications. Bokun Wang, Tianbao Yang |
| 2022 | First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson |
| 2022 | Fisher SAM: Information Geometry and Sharpness Aware Minimisation. Minyoung Kim, Da Li, Shell Xu Hu, Timothy M. Hospedales |
| 2022 | Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. Yuxin Wen, Jonas Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein |
| 2022 | Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization. Alexandre Ramé, Corentin Dancette, Matthieu Cord |
| 2022 | Flashlight: Enabling Innovation in Tools for Machine Learning. Jacob D. Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Y. Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, Benoit Steiner, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert |
| 2022 | Flow-Guided Sparse Transformer for Video Deblurring. Jing Lin, Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Youliang Yan, Xueyi Zou, Henghui Ding, Yulun Zhang, Radu Timofte, Luc Van Gool |
| 2022 | Flow-based Recurrent Belief State Learning for POMDPs. Xiaoyu Chen, Yao Mark Mu, Ping Luo, Shengbo Li, Jianyu Chen |
| 2022 | Flowformer: Linearizing Transformers with Conservation Flows. Haixu Wu, Jialong Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long |
| 2022 | Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension. Bruno Loureiro, Cédric Gerbelot, Maria Refinetti, Gabriele Sicuro, Florent Krzakala |
| 2022 | For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria. Scott Emmons, Caspar Oesterheld, Andrew Critch, Vincent Conitzer, Stuart Russell |
| 2022 | Forget-free Continual Learning with Winning Subnetworks. Haeyong Kang, Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid, Jaehong Yoon, Mark Hasegawa-Johnson, Sung Ju Hwang, Chang D. Yoo |
| 2022 | Forward Operator Estimation in Generative Models with Kernel Transfer Operators. Zhichun Huang, Rudrasis Chakraborty, Vikas Singh |
| 2022 | Fourier Learning with Cyclical Data. Yingxiang Yang, Zhihan Xiong, Tianyi Liu, Taiqing Wang, Chong Wang |
| 2022 | Framework for Evaluating Faithfulness of Local Explanations. Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz |
| 2022 | FriendlyCore: Practical Differentially Private Aggregation. Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer |
| 2022 | From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard |
| 2022 | From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model. HeeSun Bae, Seungjae Shin, Byeonghu Na, JoonHo Jang, Kyungwoo Song, Il-Chul Moon |
| 2022 | From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers. Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamás Sarlós, Adrian Weller, Thomas Weingarten |
| 2022 | From data to functa: Your data point is a function and you can treat it like one. Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo Jimenez Rezende, Dan Rosenbaum |
| 2022 | Frustratingly Easy Transferability Estimation. Long-Kai Huang, Junzhou Huang, Yu Rong, Qiang Yang, Ying Wei |
| 2022 | Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis. Sho Sonoda, Isao Ishikawa, Masahiro Ikeda |
| 2022 | Function-space Inference with Sparse Implicit Processes. Simón Rodríguez Santana, Bryan Zaldivar, Daniel Hernández-Lobato |
| 2022 | Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf |
| 2022 | Functional Output Regression with Infimal Convolution: Exploring the Huber and ε-insensitive Losses. Alex Lambert, Dimitri Bouche, Zoltán Szabó, Florence d'Alché-Buc |
| 2022 | G Mingjie Li, Xiaojun Guo, Yifei Wang, Yisen Wang, Zhouchen Lin |
| 2022 | G-Mixup: Graph Data Augmentation for Graph Classification. Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu |
| 2022 | GACT: Activation Compressed Training for Generic Network Architectures. Xiaoxuan Liu, Lianmin Zheng, Dequan Wang, Yukuo Cen, Weize Chen, Xu Han, Jianfei Chen, Zhiyuan Liu, Jie Tang, Joey Gonzalez, Michael W. Mahoney, Alvin Cheung |
| 2022 | GALAXY: Graph-based Active Learning at the Extreme. Jifan Zhang, Julian Katz-Samuels, Robert D. Nowak |
| 2022 | GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen |
| 2022 | GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten P. Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen S. Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V. Le, Yonghui Wu, Zhifeng Chen, Claire Cui |
| 2022 | GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks. Yixuan He, Quan Gan, David Wipf, Gesine D. Reinert, Junchi Yan, Mihai Cucuringu |
| 2022 | GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing. Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song, Jun Zhu |
| 2022 | Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers. Rui Liu, Young Jin Kim, Alexandre Muzio, Hany Hassan |
| 2022 | Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification. Junwen Bai, Shufeng Kong, Carla P. Gomes |
| 2022 | Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications. Alexandre Capone, Armin Lederer, Sandra Hirche |
| 2022 | GenLabel: Mixup Relabeling using Generative Models. Jy-yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris S. Papailiopoulos, Kangwook Lee |
| 2022 | General-purpose, long-context autoregressive modeling with Perceiver AR. Curtis Hawthorne, Andrew Jaegle, Catalina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew M. Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse H. Engel |
| 2022 | Generalised Policy Improvement with Geometric Policy Composition. Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto |
| 2022 | Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney |
| 2022 | Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling. Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong |
| 2022 | Generalization and Robustness Implications in Object-Centric Learning. Andrea Dittadi, Samuele S. Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, Francesco Locatello |
| 2022 | Generalized Beliefs for Cooperative AI. Darius Muglich, Luisa M. Zintgraf, Christian A. Schröder de Witt, Shimon Whiteson, Jakob N. Foerster |
| 2022 | Generalized Data Distribution Iteration. Jiajun Fan, Changnan Xiao |
| 2022 | Generalized Federated Learning via Sharpness Aware Minimization. Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, Zhuo Lu |
| 2022 | Generalized Leverage Scores: Geometric Interpretation and Applications. Bruno Ordozgoiti, Antonis Matakos, Aristides Gionis |
| 2022 | Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model. Heejong Bong, Alessandro Rinaldo |
| 2022 | Generalized Strategic Classification and the Case of Aligned Incentives. Sagi Levanon, Nir Rosenfeld |
| 2022 | Generalizing Gaussian Smoothing for Random Search. Katelyn Gao, Ozan Sener |
| 2022 | Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder. Tiexin Qin, Shiqi Wang, Haoliang Li |
| 2022 | Generalizing to New Physical Systems via Context-Informed Dynamics Model. Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà, Nicolas Baskiotis, Alain Rakotomamonjy, Patrick Gallinari |
| 2022 | Generating 3D Molecules for Target Protein Binding. Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji |
| 2022 | Generating Distributional Adversarial Examples to Evade Statistical Detectors. Yigitcan Kaya, Muhammad Bilal Zafar, Sergül Aydöre, Nathalie Rauschmayr, Krishnaram Kenthapadi |
| 2022 | Generative Coarse-Graining of Molecular Conformations. Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess E. Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli |
| 2022 | Generative Cooperative Networks for Natural Language Generation. Sylvain Lamprier, Thomas Scialom, Antoine Chaffin, Vincent Claveau, Ewa Kijak, Jacopo Staiano, Benjamin Piwowarski |
| 2022 | Generative Flow Networks for Discrete Probabilistic Modeling. Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron C. Courville, Yoshua Bengio |
| 2022 | Generative Modeling for Multi-task Visual Learning. Zhipeng Bao, Martial Hebert, Yu-Xiong Wang |
| 2022 | Generative Trees: Adversarial and Copycat. Richard Nock, Mathieu Guillame-Bert |
| 2022 | Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more. Elad Tolochinsky, Ibrahim Jubran, Dan Feldman |
| 2022 | Geometric Multimodal Contrastive Representation Learning. Petra Poklukar, Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva, Danica Kragic |
| 2022 | Global Optimization Networks. Sen Zhao, Erez Louidor, Maya R. Gupta |
| 2022 | Global Optimization of K-Center Clustering. Mingfei Shi, Kaixun Hua, Jiayang Ren, Yankai Cao |
| 2022 | Goal Misgeneralization in Deep Reinforcement Learning. Lauro Langosco di Langosco, Jack Koch, Lee D. Sharkey, Jacob Pfau, David Krueger |
| 2022 | Going Deeper into Permutation-Sensitive Graph Neural Networks. Zhongyu Huang, Yingheng Wang, Chaozhuo Li, Huiguang He |
| 2022 | Gradient Based Clustering. Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic, Soummya Kar |
| 2022 | Gradient Descent on Neurons and its Link to Approximate Second-order Optimization. Frederik Benzing |
| 2022 | Gradient-Free Method for Heavily Constrained Nonconvex Optimization. Wanli Shi, Hongchang Gao, Bin Gu |
| 2022 | Graph Neural Architecture Search Under Distribution Shifts. Yijian Qin, Xin Wang, Ziwei Zhang, Pengtao Xie, Wenwu Zhu |
| 2022 | Graph-Coupled Oscillator Networks. T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein |
| 2022 | GraphFM: Improving Large-Scale GNN Training via Feature Momentum. Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji |
| 2022 | Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning. Lipeng Wan, Zeyang Liu, Xingyu Chen, Xuguang Lan, Nanning Zheng |
| 2022 | Greedy when Sure and Conservative when Uncertain about the Opponents. Haobo Fu, Ye Tian, Hongxiang Yu, Weiming Liu, Shuang Wu, Jiechao Xiong, Ying Wen, Kai Li, Junliang Xing, Qiang Fu, Wei Yang |
| 2022 | Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan |
| 2022 | Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance. Heeseung Kim, Sungwon Kim, Sungroh Yoon |
| 2022 | H-Consistency Bounds for Surrogate Loss Minimizers. Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2022 | Hardness and Algorithms for Robust and Sparse Optimization. Eric Price, Sandeep Silwal, Samson Zhou |
| 2022 | Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning. Utku Evci, Vincent Dumoulin, Hugo Larochelle, Michael C. Mozer |
| 2022 | Hermite Polynomial Features for Private Data Generation. Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mijung Park |
| 2022 | Hessian-Free High-Resolution Nesterov Acceleration For Sampling. Ruilin Li, Hongyuan Zha, Molei Tao |
| 2022 | Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models. Abhineet Agarwal, Yan Shuo Tan, Omer Ronen, Chandan Singh, Bin Yu |
| 2022 | High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails. Shaojie Li, Yong Liu |
| 2022 | Hindering Adversarial Attacks with Implicit Neural Representations. Andrei A. Rusu, Dan Andrei Calian, Sven Gowal, Raia Hadsell |
| 2022 | History Compression via Language Models in Reinforcement Learning. Fabian Paischer, Thomas Adler, Vihang Patil, Angela Bitto-Nemling, Markus Holzleitner, Sebastian Lehner, Hamid Eghbal-zadeh, Sepp Hochreiter |
| 2022 | HousE: Knowledge Graph Embedding with Householder Parameterization. Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang |
| 2022 | How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models. Ahmed M. Alaa, Boris van Breugel, Evgeny S. Saveliev, Mihaela van der Schaar |
| 2022 | How Powerful are Spectral Graph Neural Networks. Xiyuan Wang, Muhan Zhang |
| 2022 | How Tempering Fixes Data Augmentation in Bayesian Neural Networks. Gregor Bachmann, Lorenzo Noci, Thomas Hofmann |
| 2022 | How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity. Chengyue Gong, Lemeng Wu, Qiang Liu |
| 2022 | How to Leverage Unlabeled Data in Offline Reinforcement Learning. Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine |
| 2022 | How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation. Augustine N. Mavor-Parker, Kimberly A. Young, Caswell Barry, Lewis D. Griffin |
| 2022 | How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection. Mantas Mazeika, Bo Li, David A. Forsyth |
| 2022 | How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective. Akhilan Boopathy, Ila Fiete |
| 2022 | Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, Liwei Wang |
| 2022 | HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar |
| 2022 | HyperPrompt: Prompt-based Task-Conditioning of Transformers. Yun He, Huaixiu Steven Zheng, Yi Tay, Jai Prakash Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, Yaguang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed H. Chi |
| 2022 | HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning. Andrey Zhmoginov, Mark Sandler, Maksym Vladymyrov |
| 2022 | IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data. Tian Gao, Debarun Bhattacharjya, Elliot Nelson, Miao Liu, Yue Yu |
| 2022 | IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages. Emanuele Bugliarello, Fangyu Liu, Jonas Pfeiffer, Siva Reddy, Desmond Elliott, Edoardo Maria Ponti, Ivan Vulic |
| 2022 | Identifiability Conditions for Domain Adaptation. Ishaan Gulrajani, Tatsunori Hashimoto |
| 2022 | Identification of Linear Non-Gaussian Latent Hierarchical Structure. Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang |
| 2022 | Identity-Disentangled Adversarial Augmentation for Self-supervised Learning. Kaiwen Yang, Tianyi Zhou, Xinmei Tian, Dacheng Tao |
| 2022 | Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. Anastasios N. Angelopoulos, Amit Pal Singh Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano |
| 2022 | Imitation Learning by Estimating Expertise of Demonstrators. Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani |
| 2022 | Implicit Bias of Linear Equivariant Networks. Hannah Lawrence, Bobak Toussi Kiani, Kristian G. Georgiev, Andrew K. Dienes |
| 2022 | Implicit Bias of the Step Size in Linear Diagonal Neural Networks. Mor Shpigel Nacson, Kavya Ravichandran, Nathan Srebro, Daniel Soudry |
| 2022 | Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. Noam Razin, Asaf Maman, Nadav Cohen |
| 2022 | Implicit Regularization with Polynomial Growth in Deep Tensor Factorization. Kais Hariz, Hachem Kadri, Stéphane Ayache, Maher Moakher, Thierry Artières |
| 2022 | Importance Weighted Kernel Bayes' Rule. Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton |
| 2022 | Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters. Xin Chen, Yujie Tang, Na Li |
| 2022 | Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation. Wenxiao Wang, Alexander Levine, Soheil Feizi |
| 2022 | Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning. Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu, Alberto Bernacchia |
| 2022 | Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP. Liyu Chen, Rahul Jain, Haipeng Luo |
| 2022 | Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data. Gautam Kamath, Xingtu Liu, Huanyu Zhang |
| 2022 | Improved Regret for Differentially Private Exploration in Linear MDP. Dung Daniel T. Ngo, Giuseppe Vietri, Steven Wu |
| 2022 | Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images. Rakshith Subramanyam, Vivek Sivaraman Narayanaswamy, Mark Naufel, Andreas Spanias, Jayaraman J. Thiagarajan |
| 2022 | Improving Adversarial Robustness via Mutual Information Estimation. Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu |
| 2022 | Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation. Giung Nam, Hyungi Lee, Byeongho Heo, Juho Lee |
| 2022 | Improving Language Models by Retrieving from Trillions of Tokens. Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, Laurent Sifre |
| 2022 | Improving Mini-batch Optimal Transport via Partial Transportation. Khai Nguyen, Dang Nguyen, The-Anh Vu-Le, Tung Pham, Nhat Ho |
| 2022 | Improving Out-of-Distribution Robustness via Selective Augmentation. Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn |
| 2022 | Improving Policy Optimization with Generalist-Specialist Learning. Zhiwei Jia, Xuanlin Li, Zhan Ling, Shuang Liu, Yiran Wu, Hao Su |
| 2022 | Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification. Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Bjoern M. Eskofier, Dario Zanca |
| 2022 | Improving Screening Processes via Calibrated Subset Selection. Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez |
| 2022 | Improving Task-free Continual Learning by Distributionally Robust Memory Evolution. Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Tiehang Duan, Mingchen Gao |
| 2022 | Improving Transformers with Probabilistic Attention Keys. Tam Minh Nguyen, Tan Minh Nguyen, Dung D. Le, Duy Khuong Nguyen, Viet-Anh Tran, Richard G. Baraniuk, Nhat Ho, Stanley J. Osher |
| 2022 | In defense of dual-encoders for neural ranking. Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar |
| 2022 | Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence. Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang, Mihailo R. Jovanovic |
| 2022 | Individual Preference Stability for Clustering. Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian |
| 2022 | Individual Reward Assisted Multi-Agent Reinforcement Learning. Li Wang, Yupeng Zhang, Yujing Hu, Weixun Wang, Chongjie Zhang, Yang Gao, Jianye Hao, Tangjie Lv, Changjie Fan |
| 2022 | Inducing Causal Structure for Interpretable Neural Networks. Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah D. Goodman, Christopher Potts |
| 2022 | Inductive Biases and Variable Creation in Self-Attention Mechanisms. Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Cyril Zhang |
| 2022 | Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm. Pini Zilber, Boaz Nadler |
| 2022 | Inferring Cause and Effect in the Presence of Heteroscedastic Noise. Sascha Xu, Osman Mian, Alexander Marx, Jilles Vreeken |
| 2022 | Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems. Miguel Suau, Jinke He, Matthijs T. J. Spaan, Frans A. Oliehoek |
| 2022 | Information Discrepancy in Strategic Learning. Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu, Juba Ziani |
| 2022 | Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity. Jianyi Yang, Shaolei Ren |
| 2022 | Injecting Logical Constraints into Neural Networks via Straight-Through Estimators. Zhun Yang, Joohyung Lee, Chiyoun Park |
| 2022 | Input Dependent Sparse Gaussian Processes. Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernández-Lobato |
| 2022 | Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing. Jiayin Jin, Zeru Zhang, Yang Zhou, Lingfei Wu |
| 2022 | Instance Dependent Regret Analysis of Kernelized Bandits. Shubhanshu Shekhar, Tara Javidi |
| 2022 | Instrumental Variable Regression with Confounder Balancing. Anpeng Wu, Kun Kuang, Bo Li, Fei Wu |
| 2022 | Interactive Correlation Clustering with Existential Cluster Constraints. Rico Angell, Nicholas Monath, Nishant Yadav, Andrew McCallum |
| 2022 | Interactive Inverse Reinforcement Learning for Cooperative Games. Thomas Kleine Büning, Anne-Marie George, Christos Dimitrakakis |
| 2022 | Interactively Learning Preference Constraints in Linear Bandits. David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause |
| 2022 | International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA. Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, Sivan Sabato |
| 2022 | Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. Jan MacDonald, Mathieu Besançon, Sebastian Pokutta |
| 2022 | Interpretable Off-Policy Learning via Hyperbox Search. Daniel Tschernutter, Tobias Hatt, Stefan Feuerriegel |
| 2022 | Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism. Siqi Miao, Mia Liu, Pan Li |
| 2022 | Interventional Contrastive Learning with Meta Semantic Regularizer. Wenwen Qiang, Jiangmeng Li, Changwen Zheng, Bing Su, Hui Xiong |
| 2022 | Intriguing Properties of Input-Dependent Randomized Smoothing. Peter Súkeník, Aleksei Kuvshinov, Stephan Günnemann |
| 2022 | Invariant Ancestry Search. Phillip B. Mogensen, Nikolaj Thams, Jonas Peters |
| 2022 | Inverse Contextual Bandits: Learning How Behavior Evolves over Time. Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar |
| 2022 | Investigating Generalization by Controlling Normalized Margin. Alexander R. Farhang, Jeremy D. Bernstein, Kushal Tirumala, Yang Liu, Yisong Yue |
| 2022 | Investigating Why Contrastive Learning Benefits Robustness against Label Noise. Yihao Xue, Kyle Whitecross, Baharan Mirzasoleiman |
| 2022 | It's Raw! Audio Generation with State-Space Models. Karan Goel, Albert Gu, Chris Donahue, Christopher Ré |
| 2022 | Iterative Double Sketching for Faster Least-Squares Optimization. Rui Wang, Yanyan Ouyang, Wangli Xu |
| 2022 | Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime. Kyriakos Axiotis, Maxim Sviridenko |
| 2022 | Kernel Methods for Radial Transformed Compositional Data with Many Zeros. Junyoung Park, Changwon Yoon, Cheolwoo Park, Jeongyoun Ahn |
| 2022 | Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games. Gabriele Farina, Chung-wei Lee, Haipeng Luo, Christian Kroer |
| 2022 | Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots. Yuanyuan Liu, Fanhua Shang, Weixin An, Hongying Liu, Zhouchen Lin |
| 2022 | Knowledge Base Question Answering by Case-based Reasoning over Subgraphs. Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Manzil Zaheer, Hannaneh Hajishirzi, Robin Jia, Andrew McCallum |
| 2022 | Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations. Bodhisattwa Prasad Majumder, Oana Camburu, Thomas Lukasiewicz, Julian J. McAuley |
| 2022 | Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics. Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Yoshinobu Kawahara |
| 2022 | LCANets: Lateral Competition Improves Robustness Against Corruption and Attack. Michael A. Teti, Garrett T. Kenyon, Ben Migliori, Juston Moore |
| 2022 | LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood. Piotr Tempczyk, Rafal Michaluk, Lukasz Garncarek, Przemyslaw Spurek, Jacek Tabor, Adam Golinski |
| 2022 | LIMO: Latent Inceptionism for Targeted Molecule Generation. Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael K. Gilson, Rose Yu |
| 2022 | LSB: Local Self-Balancing MCMC in Discrete Spaces. Emanuele Sansone |
| 2022 | Label Ranking through Nonparametric Regression. Dimitris Fotakis, Alkis Kalavasis, Eleni Psaroudaki |
| 2022 | Label-Descriptive Patterns and Their Application to Characterizing Classification Errors. Michael A. Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken |
| 2022 | Label-Free Explainability for Unsupervised Models. Jonathan Crabbé, Mihaela van der Schaar |
| 2022 | Lagrangian Method for Q-Function Learning (with Applications to Machine Translation). Bojun Huang |
| 2022 | Langevin Monte Carlo for Contextual Bandits. Pan Xu, Hongkai Zheng, Eric V. Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar |
| 2022 | Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents. Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch |
| 2022 | Large Batch Experience Replay. Thibault Lahire, Matthieu Geist, Emmanuel Rachelson |
| 2022 | Large-Scale Graph Neural Architecture Search. Chaoyu Guan, Xin Wang, Hong Chen, Ziwei Zhang, Wenwu Zhu |
| 2022 | Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang |
| 2022 | Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade |
| 2022 | Latent Diffusion Energy-Based Model for Interpretable Text Modelling. Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu |
| 2022 | Latent Outlier Exposure for Anomaly Detection with Contaminated Data. Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt |
| 2022 | Lazy Estimation of Variable Importance for Large Neural Networks. Yue Gao, Abby Stevens, Garvesh Raskutti, Rebecca Willett |
| 2022 | LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation. David Ireland, Giovanni Montana |
| 2022 | Learning Augmented Binary Search Trees. Honghao Lin, Tian Luo, David P. Woodruff |
| 2022 | Learning Bellman Complete Representations for Offline Policy Evaluation. Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun |
| 2022 | Learning Domain Adaptive Object Detection with Probabilistic Teacher. Meilin Chen, Weijie Chen, Shicai Yang, Jie Song, Xinchao Wang, Lei Zhang, Yunfeng Yan, Donglian Qi, Yueting Zhuang, Di Xie, Shiliang Pu |
| 2022 | Learning Dynamics and Generalization in Deep Reinforcement Learning. Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal |
| 2022 | Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks. Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos |
| 2022 | Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent. Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis |
| 2022 | Learning Infinite-horizon Average-reward Markov Decision Process with Constraints. Liyu Chen, Rahul Jain, Haipeng Luo |
| 2022 | Learning Iterative Reasoning through Energy Minimization. Yilun Du, Shuang Li, Joshua B. Tenenbaum, Igor Mordatch |
| 2022 | Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits. Qinghua Liu, Yuanhao Wang, Chi Jin |
| 2022 | Learning Mixtures of Linear Dynamical Systems. Yanxi Chen, H. Vincent Poor |
| 2022 | Learning Multiscale Transformer Models for Sequence Generation. Bei Li, Tong Zheng, Yi Jing, Chengbo Jiao, Tong Xiao, Jingbo Zhu |
| 2022 | Learning Pseudometric-based Action Representations for Offline Reinforcement Learning. Pengjie Gu, Mengchen Zhao, Chen Chen, Dong Li, Jianye Hao, Bo An |
| 2022 | Learning Stable Classifiers by Transferring Unstable Features. Yujia Bao, Shiyu Chang, Regina Barzilay |
| 2022 | Learning Stochastic Shortest Path with Linear Function Approximation. Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu |
| 2022 | Learning Symmetric Embeddings for Equivariant World Models. Jung Yeon Park, Ondrej Biza, Linfeng Zhao, Jan-Willem van de Meent, Robin Walters |
| 2022 | Learning fair representation with a parametric integral probability metric. Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim |
| 2022 | Learning from Counterfactual Links for Link Prediction. Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, Meng Jiang |
| 2022 | Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation. Zhihan Liu, Yufeng Zhang, Zuyue Fu, Zhuoran Yang, Zhaoran Wang |
| 2022 | Learning from a Learning User for Optimal Recommendations. Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu |
| 2022 | Learning inverse folding from millions of predicted structures. Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives |
| 2022 | Learning of Cluster-based Feature Importance for Electronic Health Record Time-series. Henrique Aguiar, Mauro D. Santos, Peter J. Watkinson, Tingting Zhu |
| 2022 | Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning. Max B. Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris J. Maddison |
| 2022 | Learning to Estimate and Refine Fluid Motion with Physical Dynamics. Mingrui Zhang, Jianhong Wang, James B. Tlhomole, Matthew D. Piggott |
| 2022 | Learning to Hash Robustly, Guaranteed. Alexandr Andoni, Daniel Beaglehole |
| 2022 | Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization. Xiang Gao, Yuqi Zhang, Yingjie Tian |
| 2022 | Learning to Infer Structures of Network Games. Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong |
| 2022 | Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters. Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu, Florence d'Alché-Buc |
| 2022 | Learning to Separate Voices by Spatial Regions. Alan Xu, Romit Roy Choudhury |
| 2022 | Learning to Solve PDE-constrained Inverse Problems with Graph Networks. Qingqing Zhao, David B. Lindell, Gordon Wetzstein |
| 2022 | Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training. Jan Kaiser, Oliver Stein, Annika Eichler |
| 2022 | Least Squares Estimation using Sketched Data with Heteroskedastic Errors. Sokbae Lee, Serena Ng |
| 2022 | Let Invariant Rationale Discovery Inspire Graph Contrastive Learning. Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, Tat-Seng Chua |
| 2022 | Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time. David P. Woodruff, Amir Zandieh |
| 2022 | Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity. Lin Guan, Sarath Sreedharan, Subbarao Kambhampati |
| 2022 | Lie Point Symmetry Data Augmentation for Neural PDE Solvers. Johannes Brandstetter, Max Welling, Daniel E. Worrall |
| 2022 | Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent. Pedro Soto, Ilia Ilmer, Haibin Guan, Jun Li |
| 2022 | Linear Adversarial Concept Erasure. Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan Cotterell |
| 2022 | Linear Bandit Algorithms with Sublinear Time Complexity. Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi |
| 2022 | Linear Complexity Randomized Self-attention Mechanism. Lin Zheng, Chong Wang, Lingpeng Kong |
| 2022 | Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs. Meyer Scetbon, Gabriel Peyré, Marco Cuturi |
| 2022 | Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness. Tianlong Chen, Huan Zhang, Zhenyu Zhang, Shiyu Chang, Sijia Liu, Pin-Yu Chen, Zhangyang Wang |
| 2022 | Local Augmentation for Graph Neural Networks. Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu |
| 2022 | Local Linear Convergence of Douglas-Rachford for Linear Programming: a Probabilistic Analysis. Oisin Faust, Hamza Fawzi |
| 2022 | Locally Sparse Neural Networks for Tabular Biomedical Data. Junchen Yang, Ofir Lindenbaum, Yuval Kluger |
| 2022 | Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets. Tal Shnitzer, Mikhail Yurochkin, Kristjan H. Greenewald, Justin M. Solomon |
| 2022 | Loss Function Learning for Domain Generalization by Implicit Gradient. Boyan Gao, Henry Gouk, Yongxin Yang, Timothy M. Hospedales |
| 2022 | Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions. Eunsang Lee, Joon-Woo Lee, Junghyun Lee, Young-Sik Kim, Yongjune Kim, Jong-Seon No, Woosuk Choi |
| 2022 | Low-Precision Stochastic Gradient Langevin Dynamics. Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa |
| 2022 | LyaNet: A Lyapunov Framework for Training Neural ODEs. Ivan Dario Jimenez Rodriguez, Aaron D. Ames, Yisong Yue |
| 2022 | Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control. Katie Kang, Paula Gradu, Jason J. Choi, Michael Janner, Claire J. Tomlin, Sergey Levine |
| 2022 | MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection. Zhenhong Sun, Ming Lin, Xiuyu Sun, Zhiyu Tan, Hao Li, Rong Jin |
| 2022 | MAML and ANIL Provably Learn Representations. Liam Collins, Aryan Mokhtari, Sewoong Oh, Sanjay Shakkottai |
| 2022 | MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer. Jeewon Jeon, Woojun Kim, Whiyoung Jung, Youngchul Sung |
| 2022 | ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases. Jintai Chen, Kuanlun Liao, Kun Wei, Haochao Ying, Danny Z. Chen, Jian Wu |
| 2022 | Making Linear MDPs Practical via Contrastive Representation Learning. Tianjun Zhang, Tongzheng Ren, Mengjiao Yang, Joseph Gonzalez, Dale Schuurmans, Bo Dai |
| 2022 | Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization. Hanjun Dai, Mengjiao Yang, Yuan Xue, Dale Schuurmans, Bo Dai |
| 2022 | Marginal Tail-Adaptive Normalizing Flows. Mike Laszkiewicz, Johannes Lederer, Asja Fischer |
| 2022 | Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems. Lukas Köhs, Bastian Alt, Heinz Koeppl |
| 2022 | Maslow's Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation. Sebastian Lee, Stefano Sarao Mannelli, Claudia Clopath, Sebastian Goldt, Andrew M. Saxe |
| 2022 | Massively Parallel k-Means Clustering for Perturbation Resilient Instances. Vincent Cohen-Addad, Vahab S. Mirrokni, Peilin Zhong |
| 2022 | Matching Learned Causal Effects of Neural Networks with Domain Priors. Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N. Balasubramanian, Amit Sharma |
| 2022 | Matching Normalizing Flows and Probability Paths on Manifolds. Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Maximilian Nickel, Aditya Grover, Ricky T. Q. Chen, Yaron Lipman |
| 2022 | Matching Structure for Dual Learning. Hao Fei, Shengqiong Wu, Yafeng Ren, Meishan Zhang |
| 2022 | Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching. Cheng Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu |
| 2022 | Meaningfully debugging model mistakes using conceptual counterfactual explanations. Abubakar Abid, Mert Yüksekgönül, James Zou |
| 2022 | Measure Estimation in the Barycentric Coding Model. Matthew Werenski, Ruijie Jiang, Abiy Tasissa, Shuchin Aeron, James M. Murphy |
| 2022 | Measuring Representational Robustness of Neural Networks Through Shared Invariances. Vedant Nanda, Till Speicher, Camila Kolling, John P. Dickerson, Krishna P. Gummadi, Adrian Weller |
| 2022 | Measuring dissimilarity with diffeomorphism invariance. Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi |
| 2022 | Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments. Jinkun Lin, Anqi Zhang, Mathias Lécuyer, Jinyang Li, Aurojit Panda, Siddhartha Sen |
| 2022 | MemSR: Training Memory-efficient Lightweight Model for Image Super-Resolution. Kailu Wu, Chung-Kuei Lee, Kaisheng Ma |
| 2022 | Memory-Based Model Editing at Scale. Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D. Manning, Chelsea Finn |
| 2022 | MetAug: Contrastive Learning via Meta Feature Augmentation. Jiangmeng Li, Wenwen Qiang, Changwen Zheng, Bing Su, Hui Xiong |
| 2022 | Meta-Learning Hypothesis Spaces for Sequential Decision-making. Parnian Kassraie, Jonas Rothfuss, Andreas Krause |
| 2022 | Metric-Fair Active Learning. Jie Shen, Nan Cui, Jing Wang |
| 2022 | Metric-Fair Classifier Derandomization. Jimmy Wu, Yatong Chen, Yang Liu |
| 2022 | Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees. Verónica Álvarez, Santiago Mazuelas, José Antonio Lozano |
| 2022 | Minimax M-estimation under Adversarial Contamination. Sujay Bhatt, Guanhua Fang, Ping Li, Gennady Samorodnitsky |
| 2022 | Minimizing Control for Credit Assignment with Strong Feedback. Alexander Meulemans, Matilde Tristany Farinha, Maria R. Cervera, João Sacramento, Benjamin F. Grewe |
| 2022 | Minimum Cost Intervention Design for Causal Effect Identification. Sina Akbari, Jalal Etesami, Negar Kiyavash |
| 2022 | Mirror Learning: A Unifying Framework of Policy Optimisation. Jakub Grudzien Kuba, Christian A. Schröder de Witt, Jakob N. Foerster |
| 2022 | Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model. Jean-Rémy Conti, Nathan Noiry, Stéphan Clémençon, Vincent Despiegel, Stéphane Gentric |
| 2022 | Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization. Adrián Javaloy, Maryam Meghdadi, Isabel Valera |
| 2022 | Mitigating Neural Network Overconfidence with Logit Normalization. Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, Yixuan Li |
| 2022 | ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias. Yupu Lu, Shijie Lin, Guanqi Chen, Jia Pan |
| 2022 | Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably). Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, Longbo Huang |
| 2022 | Model Agnostic Sample Reweighting for Out-of-Distribution Learning. Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, Tong Zhang |
| 2022 | Model Selection in Batch Policy Optimization. Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai |
| 2022 | Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt |
| 2022 | Model-Free Opponent Shaping. Christopher Lu, Timon Willi, Christian A. Schröder de Witt, Jakob N. Foerster |
| 2022 | Model-Value Inconsistency as a Signal for Epistemic Uncertainty. Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal M. P. Behbahani, Tom Schaul, André Barreto, Simon Osindero |
| 2022 | Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search. Qi Wang, Herke van Hoof |
| 2022 | Modeling Adversarial Noise for Adversarial Training. Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu |
| 2022 | Modeling Irregular Time Series with Continuous Recurrent Units. Mona Schirmer, Mazin Eltayeb, Stefan Lessmann, Maja Rudolph |
| 2022 | Modeling Strong and Human-Like Gameplay with KL-Regularized Search. Athul Paul Jacob, David J. Wu, Gabriele Farina, Adam Lerer, Hengyuan Hu, Anton Bakhtin, Jacob Andreas, Noam Brown |
| 2022 | Modeling Structure with Undirected Neural Networks. Tsvetomila Mihaylova, Vlad Niculae, André F. T. Martins |
| 2022 | Modular Conformal Calibration. Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon |
| 2022 | Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks. Zhaoning Yu, Hongyang Gao |
| 2022 | Monarch: Expressive Structured Matrices for Efficient and Accurate Training. Tri Dao, Beidi Chen, Nimit Sharad Sohoni, Arjun D. Desai, Michael Poli, Jessica Grogan, Alexander Liu, Aniruddh Rao, Atri Rudra, Christopher Ré |
| 2022 | More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees. Osman Asif Malik |
| 2022 | More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize. Alexander Wei, Wei Hu, Jacob Steinhardt |
| 2022 | Multi Resolution Analysis (MRA) for Approximate Self-Attention. Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn Moo Fung, Vikas Singh |
| 2022 | Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts. Yan Zeng, Xinsong Zhang, Hang Li |
| 2022 | Multi-Level Branched Regularization for Federated Learning. Jinkyu Kim, Geeho Kim, Bohyung Han |
| 2022 | Multi-Task Learning as a Bargaining Game. Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya |
| 2022 | Multi-scale Feature Learning Dynamics: Insights for Double Descent. Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie |
| 2022 | Multi-slots Online Matching with High Entropy. Xingyu Lu, Qintong Wu, Wenliang Zhong |
| 2022 | Multiclass learning with margin: exponential rates with no bias-variance trade-off. Stefano Vigogna, Giacomo Meanti, Ernesto De Vito, Lorenzo Rosasco |
| 2022 | Multicoated Supermasks Enhance Hidden Networks. Yasuyuki Okoshi, Ángel López García-Arias, Kazutoshi Hirose, Kota Ando, Kazushi Kawamura, Thiem Van Chu, Masato Motomura, Jaehoon Yu |
| 2022 | Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms. Xuchuang Wang, Hong Xie, John C. S. Lui |
| 2022 | Multirate Training of Neural Networks. Tiffany J. Vlaar, Benedict J. Leimkuhler |
| 2022 | N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations. Qingyang Tan, Zherong Pan, Breannan Smith, Takaaki Shiratori, Dinesh Manocha |
| 2022 | NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning. Wentao Zhang, Zeang Sheng, Mingyu Yang, Yang Li, Yu Shen, Zhi Yang, Bin Cui |
| 2022 | NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks. Mustafa Burak Gurbuz, Constantine Dovrolis |
| 2022 | NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework. Xingcheng Yao, Yanan Zheng, Xiaocong Yang, Zhilin Yang |
| 2022 | NOMU: Neural Optimization-based Model Uncertainty. Jakob Heiss, Jakob Weissteiner, Hanna S. Wutte, Sven Seuken, Josef Teichmann |
| 2022 | NP-Match: When Neural Processes meet Semi-Supervised Learning. Jianfeng Wang, Thomas Lukasiewicz, Daniela Massiceti, Xiaolin Hu, Vladimir Pavlovic, Alexandros Neophytou |
| 2022 | Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. Martin Genzel, Ingo Gühring, Jan MacDonald, Maximilian März |
| 2022 | Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path. Haoyuan Cai, Tengyu Ma, Simon S. Du |
| 2022 | Near-Optimal Learning of Extensive-Form Games with Imperfect Information. Yu Bai, Chi Jin, Song Mei, Tiancheng Yu |
| 2022 | Near-optimal rate of consistency for linear models with missing values. Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet |
| 2022 | Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation. Pihe Hu, Yu Chen, Longbo Huang |
| 2022 | Nearly Optimal Catoni's M-estimator for Infinite Variance. Sujay Bhatt, Guanhua Fang, Ping Li, Gennady Samorodnitsky |
| 2022 | Nearly Optimal Policy Optimization with Stable at Any Time Guarantee. Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon S. Du, Jiantao Jiao |
| 2022 | Nested Bandits. Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier, Houssam Zenati |
| 2022 | Nesterov Accelerated Shuffling Gradient Method for Convex Optimization. Trang H. Tran, Katya Scheinberg, Lam M. Nguyen |
| 2022 | Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time. Burak Bartan, Mert Pilanci |
| 2022 | Neural Implicit Dictionary Learning via Mixture-of-Expert Training. Peihao Wang, Zhiwen Fan, Tianlong Chen, Zhangyang Wang |
| 2022 | Neural Inverse Kinematic. Raphael Bensadoun, Shir Gur, Nitsan Blau, Lior Wolf |
| 2022 | Neural Inverse Transform Sampler. Henry Li, Yuval Kluger |
| 2022 | Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps. Alexandre Pasquiou, Yair Lakretz, John T. Hale, Bertrand Thirion, Christophe Pallier |
| 2022 | Neural Laplace: Learning diverse classes of differential equations in the Laplace domain. Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar |
| 2022 | Neural Network Poisson Models for Behavioural and Neural Spike Train Data. Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan, Amir Dezfouli |
| 2022 | Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks. Franco Pellegrini, Giulio Biroli |
| 2022 | Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective. Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie |
| 2022 | Neural Tangent Kernel Analysis of Deep Narrow Neural Networks. Jongmin Lee, Joo Young Choi, Ernest K. Ryu, Albert No |
| 2022 | Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization. Mariia Seleznova, Gitta Kutyniok |
| 2022 | Neural Tangent Kernel Empowered Federated Learning. Kai Yue, Richeng Jin, Ryan Pilgrim, Chau-Wai Wong, Dror Baron, Huaiyu Dai |
| 2022 | Neural-Symbolic Models for Logical Queries on Knowledge Graphs. Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang, Jian Tang |
| 2022 | NeuralEF: Deconstructing Kernels by Deep Neural Networks. Zhijie Deng, Jiaxin Shi, Jun Zhu |
| 2022 | Neuro-Symbolic Hierarchical Rule Induction. Claire Glanois, Zhaohui Jiang, Xuening Feng, Paul Weng, Matthieu Zimmer, Dong Li, Wulong Liu, Jianye Hao |
| 2022 | Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval. Uri Alon, Frank F. Xu, Junxian He, Sudipta Sengupta, Dan Roth, Graham Neubig |
| 2022 | NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields. Shanyan Guan, Huayu Deng, Yunbo Wang, Xiaokang Yang |
| 2022 | Neurocoder: General-Purpose Computation Using Stored Neural Programs. Hung Le, Svetha Venkatesh |
| 2022 | Neuron Dependency Graphs: A Causal Abstraction of Neural Networks. Yaojie Hu, Jin Tian |
| 2022 | Neurotoxin: Durable Backdoors in Federated Learning. Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Prateek Mittal, Kannan Ramchandran, Joseph Gonzalez |
| 2022 | No-Regret Learning in Partially-Informed Auctions. Wenshuo Guo, Michael I. Jordan, Ellen Vitercik |
| 2022 | No-Regret Learning in Time-Varying Zero-Sum Games. Mengxiao Zhang, Peng Zhao, Haipeng Luo, Zhi-Hua Zhou |
| 2022 | Non-Vacuous Generalisation Bounds for Shallow Neural Networks. Felix Biggs, Benjamin Guedj |
| 2022 | Nonlinear Feature Diffusion on Hypergraphs. Konstantin Prokopchik, Austin R. Benson, Francesco Tudisco |
| 2022 | Nonparametric Embeddings of Sparse High-Order Interaction Events. Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe |
| 2022 | Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition. Zheng Wang, Shandian Zhe |
| 2022 | Nonparametric Involutive Markov Chain Monte Carlo. Carol Mak, Fabian Zaiser, Luke Ong |
| 2022 | Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes. Conor Tillinghast, Zheng Wang, Shandian Zhe |
| 2022 | Not All Poisons are Created Equal: Robust Training against Data Poisoning. Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman |
| 2022 | NysADMM: faster composite convex optimization via low-rank approximation. Shipu Zhao, Zachary Frangella, Madeleine Udell |
| 2022 | Nyström Kernel Mean Embeddings. Antoine Chatalic, Nicolas Schreuder, Lorenzo Rosasco, Alessandro Rudi |
| 2022 | OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework. Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, Hongxia Yang |
| 2022 | Object Permanence Emerges in a Random Walk along Memory. Pavel Tokmakov, Allan Jabri, Jie Li, Adrien Gaidon |
| 2022 | Off-Policy Evaluation for Large Action Spaces via Embeddings. Yuta Saito, Thorsten Joachims |
| 2022 | Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory. Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni, Mengdi Wang |
| 2022 | Off-Policy Reinforcement Learning with Delayed Rewards. Beining Han, Zhizhou Ren, Zuofan Wu, Yuan Zhou, Jian Peng |
| 2022 | Offline Meta-Reinforcement Learning with Online Self-Supervision. Vitchyr H. Pong, Ashvin Nair, Laura Smith, Catherine Huang, Sergey Levine |
| 2022 | Offline RL Policies Should Be Trained to be Adaptive. Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, Sergey Levine |
| 2022 | Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning. Ling Yang, Shenda Hong |
| 2022 | On Collective Robustness of Bagging Against Data Poisoning. Ruoxin Chen, Zenan Li, Jie Li, Junchi Yan, Chentao Wu |
| 2022 | On Convergence of Gradient Descent Ascent: A Tight Local Analysis. Haochuan Li, Farzan Farnia, Subhro Das, Ali Jadbabaie |
| 2022 | On Distribution Shift in Learning-based Bug Detectors. Jingxuan He, Luca Beurer-Kellner, Martin T. Vechev |
| 2022 | On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis. Qi Lyu, Xiao Fu |
| 2022 | On Implicit Bias in Overparameterized Bilevel Optimization. Paul Vicol, Jonathan P. Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse |
| 2022 | On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning. Weichao Mao, Lin Yang, Kaiqing Zhang, Tamer Basar |
| 2022 | On Last-Iterate Convergence Beyond Zero-Sum Games. Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina, Tuomas Sandholm |
| 2022 | On Learning Mixture of Linear Regressions in the Non-Realizable Setting. Soumyabrata Pal, Arya Mazumdar, Rajat Sen, Avishek Ghosh |
| 2022 | On Measuring Causal Contributions via do-interventions. Yonghan Jung, Shiva Prasad Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim |
| 2022 | On Non-local Convergence Analysis of Deep Linear Networks. Kun Chen, Dachao Lin, Zhihua Zhang |
| 2022 | On Numerical Integration in Neural Ordinary Differential Equations. Aiqing Zhu, Pengzhan Jin, Beibei Zhu, Yifa Tang |
| 2022 | On Transportation of Mini-batches: A Hierarchical Approach. Khai Nguyen, Dang Nguyen, Quoc Dinh Nguyen, Tung Pham, Hung Bui, Dinh Phung, Trung Le, Nhat Ho |
| 2022 | On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation. Xiaohong Chen, Zhengling Qi |
| 2022 | On the Adversarial Robustness of Causal Algorithmic Recourse. Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Bernhard Schölkopf |
| 2022 | On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming. Gregory Dexter, Agniva Chowdhury, Haim Avron, Petros Drineas |
| 2022 | On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum. Hongchang Gao, Junyi Li, Heng Huang |
| 2022 | On the Convergence of the Shapley Value in Parametric Bayesian Learning Games. Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low |
| 2022 | On the Difficulty of Defending Self-Supervised Learning against Model Extraction. Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot |
| 2022 | On the Effects of Artificial Data Modification. Antonia Marcu, Adam Prügel-Bennett |
| 2022 | On the Equivalence Between Temporal and Static Equivariant Graph Representations. Jianfei Gao, Bruno Ribeiro |
| 2022 | On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions. Lai Tian, Kaiwen Zhou, Anthony Man-Cho So |
| 2022 | On the Finite-Time Performance of the Knowledge Gradient Algorithm. Yanwen Li, Siyang Gao |
| 2022 | On the Generalization Analysis of Adversarial Learning. Waleed Mustafa, Yunwen Lei, Marius Kloft |
| 2022 | On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces. Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian M. Sadler, Pratap Tokekar, Alec Koppel |
| 2022 | On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games. Robert Tyler Loftin, Frans A. Oliehoek |
| 2022 | On the Learning of Non-Autoregressive Transformers. Fei Huang, Tianhua Tao, Hao Zhou, Lei Li, Minlie Huang |
| 2022 | On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features. Jinxin Zhou, Xiao Li, Tianyu Ding, Chong You, Qing Qu, Zhihui Zhu |
| 2022 | On the Practicality of Deterministic Epistemic Uncertainty. Janis Postels, Mattia Segù, Tao Sun, Luca Daniel Sieber, Luc Van Gool, Fisher Yu, Federico Tombari |
| 2022 | On the Robustness of CountSketch to Adaptive Inputs. Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer |
| 2022 | On the Role of Discount Factor in Offline Reinforcement Learning. Hao Hu, Yiqin Yang, Qianchuan Zhao, Chongjie Zhang |
| 2022 | On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs. Yuanzhou Chen, Jiafan He, Quanquan Gu |
| 2022 | On the Statistical Benefits of Curriculum Learning. Ziping Xu, Ambuj Tewari |
| 2022 | On the Surrogate Gap between Contrastive and Supervised Losses. Han Bao, Yoshihiro Nagano, Kento Nozawa |
| 2022 | One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes. Aravind Reddy, Ryan A. Rossi, Zhao Song, Anup B. Rao, Tung Mai, Nedim Lipka, Gang Wu, Eunyee Koh, Nesreen K. Ahmed |
| 2022 | One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams. Benjamin Coleman, Benito Geordie, Li Chou, Ryan A. Leo Elworth, Todd J. Treangen, Anshumali Shrivastava |
| 2022 | Online Active Regression. Cheng Chen, Yi Li, Yiming Sun |
| 2022 | Online Algorithms with Multiple Predictions. Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi |
| 2022 | Online Balanced Experimental Design. David Arbour, Drew Dimmery, Tung Mai, Anup B. Rao |
| 2022 | Online Continual Learning through Mutual Information Maximization. Yiduo Guo, Bing Liu, Dongyan Zhao |
| 2022 | Online Decision Transformer. Qinqing Zheng, Amy Zhang, Aditya Grover |
| 2022 | Online Learning and Pricing with Reusable Resources: Linear Bandits with Sub-Exponential Rewards. Huiwen Jia, Cong Shi, Siqian Shen |
| 2022 | Online Learning for Min Sum Set Cover and Pandora's Box. Evangelia Gergatsouli, Christos Tzamos |
| 2022 | Online Learning with Knapsacks: the Best of Both Worlds. Matteo Castiglioni, Andrea Celli, Christian Kroer |
| 2022 | Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan |
| 2022 | Online and Consistent Correlation Clustering. Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis |
| 2022 | Only tails matter: Average-Case Universality and Robustness in the Convex Regime. Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette |
| 2022 | Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets. Hongxin Wei, Lue Tao, Renchunzi Xie, Lei Feng, Bo An |
| 2022 | Optimal Algorithms for Mean Estimation under Local Differential Privacy. Hilal Asi, Vitaly Feldman, Kunal Talwar |
| 2022 | Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang |
| 2022 | Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training. Charbel Sakr, Steve Dai, Rangharajan Venkatesan, Brian Zimmer, William J. Dally, Brucek Khailany |
| 2022 | Optimal Clustering with Noisy Queries via Multi-Armed Bandit. Jinghui Xia, Zengfeng Huang |
| 2022 | Optimal Estimation of Policy Gradient via Double Fitted Iteration. Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang, Mengdi Wang |
| 2022 | Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits. Aadirupa Saha, Shubham Gupta |
| 2022 | Optimally Controllable Perceptual Lossy Compression. Zeyu Yan, Fei Wen, Peilin Liu |
| 2022 | Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer. Lucas Nunes Alegre, Ana L. C. Bazzan, Bruno C. da Silva |
| 2022 | Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training. Risheng Liu, Xuan Liu, Shangzhi Zeng, Jin Zhang, Yixuan Zhang |
| 2022 | Optimization-Induced Graph Implicit Nonlinear Diffusion. Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin |
| 2022 | Optimizing Sequential Experimental Design with Deep Reinforcement Learning. Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli |
| 2022 | Optimizing Tensor Network Contraction Using Reinforcement Learning. Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik |
| 2022 | Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering. Ekdeep Singh Lubana, Chi Ian Tang, Fahim Kawsar, Robert P. Dick, Akhil Mathur |
| 2022 | Order Constraints in Optimal Transport. Fabian Lim, Laura Wynter, Shiau Hong Lim |
| 2022 | Out-of-Distribution Detection with Deep Nearest Neighbors. Yiyou Sun, Yifei Ming, Xiaojin Zhu, Yixuan Li |
| 2022 | Overcoming Oscillations in Quantization-Aware Training. Markus Nagel, Marios Fournarakis, Yelysei Bondarenko, Tijmen Blankevoort |
| 2022 | PAC-Bayesian Bounds on Rate-Efficient Classifiers. Alhabib Abbas, Yiannis Andreopoulos |
| 2022 | PAC-Net: A Model Pruning Approach to Inductive Transfer Learning. Sanghoon Myung, In Huh, Wonik Jang, Jae Myung Choe, Jisu Ryu, Daesin Kim, Kee-Eung Kim, Changwook Jeong |
| 2022 | PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs. Zehao Dong, Muhan Zhang, Fuhai Li, Yixin Chen |
| 2022 | PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation. Matilde Gargiani, Andrea Zanelli, Andrea Martinelli, Tyler H. Summers, John Lygeros |
| 2022 | PDE-Based Optimal Strategy for Unconstrained Online Learning. Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis |
| 2022 | PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs. Zhengyang Shen, Tao Hong, Qi She, Jinwen Ma, Zhouchen Lin |
| 2022 | PINs: Progressive Implicit Networks for Multi-Scale Neural Representations. Zoe Landgraf, Alexander Sorkine-Hornung, Ricardo Silveira Cabral |
| 2022 | PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information. Changbin Li, Suraj Kothawade, Feng Chen, Rishabh K. Iyer |
| 2022 | PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance. Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, Tuo Zhao |
| 2022 | PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration. Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Wenyuan Tao, Zhen Wang |
| 2022 | POEM: Out-of-Distribution Detection with Posterior Sampling. Yifei Ming, Ying Fan, Yixuan Li |
| 2022 | POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging. Shishir G. Patil, Paras Jain, Prabal Dutta, Ion Stoica, Joseph Gonzalez |
| 2022 | Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding. Kazuma Tsuji, Ken'ichiro Tanaka, Sebastian Pokutta |
| 2022 | Parametric Visual Program Induction with Function Modularization. Xuguang Duan, Xin Wang, Ziwei Zhang, Wenwu Zhu |
| 2022 | Parsimonious Learning-Augmented Caching. Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit |
| 2022 | Partial Counterfactual Identification from Observational and Experimental Data. Junzhe Zhang, Jin Tian, Elias Bareinboim |
| 2022 | Partial Label Learning via Label Influence Function. Xiuwen Gong, Dong Yuan, Wei Bao |
| 2022 | Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition. Haotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex J. Smola, Zhangyang Wang |
| 2022 | Partial disentanglement for domain adaptation. Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang |
| 2022 | Particle Transformer for Jet Tagging. Huilin Qu, Congqiao Li, Sitian Qian |
| 2022 | Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules. Juhwan Noh, Dae-Woong Jeong, Kiyoung Kim, Sehui Han, Moontae Lee, Honglak Lee, Yousung Jung |
| 2022 | Path-Gradient Estimators for Continuous Normalizing Flows. Lorenz Vaitl, Kim Andrea Nicoli, Shinichi Nakajima, Pan Kessel |
| 2022 | Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning. Yang Zhao, Hao Zhang, Xiuyuan Hu |
| 2022 | Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning. Mayee F. Chen, Daniel Y. Fu, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian, Christopher Ré |
| 2022 | Permutation Search of Tensor Network Structures via Local Sampling. Chao Li, Junhua Zeng, Zerui Tao, Qibin Zhao |
| 2022 | Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu |
| 2022 | Personalized Federated Learning through Local Memorization. Othmane Marfoq, Giovanni Neglia, Richard Vidal, Laetitia Kameni |
| 2022 | Personalized Federated Learning via Variational Bayesian Inference. Xu Zhang, Yinchuan Li, Wenpeng Li, Kaiyang Guo, Yunfeng Shao |
| 2022 | Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning. Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang |
| 2022 | Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets. Han Zhong, Wei Xiong, Jiyuan Tan, Liwei Wang, Tong Zhang, Zhaoran Wang, Zhuoran Yang |
| 2022 | Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity. Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi |
| 2022 | Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning. Yunfei Li, Tian Gao, Jiaqi Yang, Huazhe Xu, Yi Wu |
| 2022 | Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification. Ling Pan, Longbo Huang, Tengyu Ma, Huazhe Xu |
| 2022 | Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization. Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao, Yong Yu, Jun Wang |
| 2022 | Planning with Diffusion for Flexible Behavior Synthesis. Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine |
| 2022 | Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks. Lukas Struppek, Dominik Hintersdorf, Antonio De Almeida Correia, Antonia Adler, Kristian Kersting |
| 2022 | Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein |
| 2022 | PoF: Post-Training of Feature Extractor for Improving Generalization. Ikuro Sato, Ryota Yamada, Masayuki Tanaka, Nakamasa Inoue, Rei Kawakami |
| 2022 | Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets. Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, Jianzhu Ma |
| 2022 | Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL. Siyi Hu, Chuanlong Xie, Xiaodan Liang, Xiaojun Chang |
| 2022 | Policy Gradient Method For Robust Reinforcement Learning. Yue Wang, Shaofeng Zou |
| 2022 | Popular decision tree algorithms are provably noise tolerant. Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan |
| 2022 | Position Prediction as an Effective Pretraining Strategy. Shuangfei Zhai, Navdeep Jaitly, Jason Ramapuram, Dan Busbridge, Tatiana Likhomanenko, Joseph Y. Cheng, Walter Talbott, Chen Huang, Hanlin Goh, Joshua M. Susskind |
| 2022 | Power-Law Escape Rate of SGD. Takashi Mori, Liu Ziyin, Kangqiao Liu, Masahito Ueda |
| 2022 | Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering. Lorenzo Orecchia, Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar |
| 2022 | Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner |
| 2022 | Predicting Out-of-Distribution Error with the Projection Norm. Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt |
| 2022 | Principal Component Flows. Edmond Cunningham, Adam D. Cobb, Susmit Jha |
| 2022 | Principled Knowledge Extrapolation with GANs. Ruili Feng, Jie Xiao, Kecheng Zheng, Deli Zhao, Jingren Zhou, Qibin Sun, Zheng-Jun Zha |
| 2022 | Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt. Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal |
| 2022 | Privacy for Free: How does Dataset Condensation Help Privacy? Tian Dong, Bo Zhao, Lingjuan Lyu |
| 2022 | Private Adaptive Optimization with Side information. Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith |
| 2022 | Private Streaming SCO in ℓ Yuxuan Han, Zhicong Liang, Zhipeng Liang, Yang Wang, Yuan Yao, Jiheng Zhang |
| 2022 | Private frequency estimation via projective geometry. Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar |
| 2022 | Private optimization in the interpolation regime: faster rates and hardness results. Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi |
| 2022 | ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning. Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, Stan Z. Li |
| 2022 | Probabilistic Bilevel Coreset Selection. Xiao Zhou, Renjie Pi, Weizhong Zhang, Yong Lin, Zonghao Chen, Tong Zhang |
| 2022 | Probabilistic ODE Solutions in Millions of Dimensions. Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig |
| 2022 | Probabilistically Robust Learning: Balancing Average and Worst-case Performance. Alexander Robey, Luiz F. O. Chamon, George J. Pappas, Hamed Hassani |
| 2022 | ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training. Hui-Po Wang, Sebastian U. Stich, Yang He, Mario Fritz |
| 2022 | Prompting Decision Transformer for Few-Shot Policy Generalization. Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua B. Tenenbaum, Chuang Gan |
| 2022 | Prototype Based Classification from Hierarchy to Fairness. Mycal Tucker, Julie A. Shah |
| 2022 | Prototype-Anchored Learning for Learning with Imperfect Annotations. Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji |
| 2022 | Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Lojasiewicz Functions when the Non-Convexity is Averaged-Out. Jun-Kun Wang, Chi-Heng Lin, Andre Wibisono, Bin Hu |
| 2022 | Provable Domain Generalization via Invariant-Feature Subspace Recovery. Haoxiang Wang, Haozhe Si, Bo Li, Han Zhao |
| 2022 | Provable Reinforcement Learning with a Short-Term Memory. Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi |
| 2022 | Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang |
| 2022 | Provably Adversarially Robust Nearest Prototype Classifiers. Václav Vorácek, Matthias Hein |
| 2022 | Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes. Hongyi Guo, Qi Cai, Yufeng Zhang, Zhuoran Yang, Zhaoran Wang |
| 2022 | Proving Theorems using Incremental Learning and Hindsight Experience Replay. Eser Aygün, Ankit Anand, Laurent Orseau, Xavier Glorot, Stephen Marcus McAleer, Vlad Firoiu, Lei M. Zhang, Doina Precup, Shibl Mourad |
| 2022 | ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik |
| 2022 | Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization. Samuel Hurault, Arthur Leclaire, Nicolas Papadakis |
| 2022 | Proximal Exploration for Model-guided Protein Sequence Design. Zhizhou Ren, Jiahan Li, Fan Ding, Yuan Zhou, Jianzhu Ma, Jian Peng |
| 2022 | Proximal and Federated Random Reshuffling. Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik |
| 2022 | Public Data-Assisted Mirror Descent for Private Model Training. Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta |
| 2022 | Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images. Shiran Zada, Itay Benou, Michal Irani |
| 2022 | QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning. Liping Yi, Gang Wang, Xiaoguang Liu |
| 2022 | Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features. Rahul Mazumder, Xiang Meng, Haoyue Wang |
| 2022 | Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding. Haotian Ma, Hao Zhang, Fan Zhou, Yinqing Zhang, Quanshi Zhang |
| 2022 | Quantifying and Learning Linear Symmetry-Based Disentanglement. Loek Tonnaer, Luis Armando Pérez Rey, Vlado Menkovski, Mike Holenderski, Jim Portegies |
| 2022 | Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra. Nadiia Chepurko, Kenneth L. Clarkson, Lior Horesh, Honghao Lin, David P. Woodruff |
| 2022 | Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization. Deokjae Lee, Seungyong Moon, Junhyeok Lee, Hyun Oh Song |
| 2022 | RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford |
| 2022 | REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer. Xingyu Liu, Deepak Pathak, Kris Kitani |
| 2022 | ROCK: Causal Inference Principles for Reasoning about Commonsense Causality. Jiayao Zhang, Hongming Zhang, Weijie J. Su, Dan Roth |
| 2022 | RUMs from Head-to-Head Contests. Matteo Almanza, Flavio Chierichetti, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins |
| 2022 | Random Forest Density Estimation. Hongwei Wen, Hanyuan Hang |
| 2022 | Random Gegenbauer Features for Scalable Kernel Methods. Insu Han, Amir Zandieh, Haim Avron |
| 2022 | RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression. Yu Gong, Greg Mori, Frederick Tung |
| 2022 | Re-evaluating Word Mover's Distance. Ryoma Sato, Makoto Yamada, Hisashi Kashima |
| 2022 | Reachability Constrained Reinforcement Learning. Dongjie Yu, Haitong Ma, Sheng-bo Li, Jianyu Chen |
| 2022 | Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series. Daniel Kramer, Philine Lou Bommer, Daniel Durstewitz, Carlo Tombolini, Georgia Koppe |
| 2022 | Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs. Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov |
| 2022 | Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks. Litian Liang, Yaosheng Xu, Stephen McAleer, Dailin Hu, Alexander Ihler, Pieter Abbeel, Roy Fox |
| 2022 | Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models. Tudor A. Manole, Nhat Ho |
| 2022 | Region-Based Semantic Factorization in GANs. Jiapeng Zhu, Yujun Shen, Yinghao Xu, Deli Zhao, Qifeng Chen |
| 2022 | Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation. Daniel Vial, Advait Parulekar, Sanjay Shakkottai, R. Srikant |
| 2022 | Regret Minimization with Performative Feedback. Meena Jagadeesan, Tijana Zrnic, Celestine Mendler-Dünner |
| 2022 | Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning. Shentao Yang, Yihao Feng, Shujian Zhang, Mingyuan Zhou |
| 2022 | Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency. Qi Cai, Zhuoran Yang, Zhaoran Wang |
| 2022 | Reinforcement Learning with Action-Free Pre-Training from Videos. Younggyo Seo, Kimin Lee, Stephen James, Pieter Abbeel |
| 2022 | Removing Batch Normalization Boosts Adversarial Training. Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, Zhangyang Wang |
| 2022 | Representation Topology Divergence: A Method for Comparing Neural Network Representations. Serguei Barannikov, Ilya Trofimov, Nikita Balabin, Evgeny Burnaev |
| 2022 | Residual-Based Sampling for Online Outlier-Robust PCA. Tianhao Zhu, Jie Shen |
| 2022 | Resilient and Communication Efficient Learning for Heterogeneous Federated Systems. Zhuangdi Zhu, Junyuan Hong, Steve Drew, Jiayu Zhou |
| 2022 | Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the O(ε Huan Li, Zhouchen Lin |
| 2022 | Rethinking Attention-Model Explainability through Faithfulness Violation Test. Yibing Liu, Haoliang Li, Yangyang Guo, Chenqi Kong, Jing Li, Shiqi Wang |
| 2022 | Rethinking Fano's Inequality in Ensemble Learning. Terufumi Morishita, Gaku Morio, Shota Horiguchi, Hiroaki Ozaki, Nobuo Nukaga |
| 2022 | Rethinking Graph Neural Networks for Anomaly Detection. Jianheng Tang, Jiajin Li, Ziqi Gao, Jia Li |
| 2022 | Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems. Yue Gao, Ilia Shumailov, Kassem Fawaz |
| 2022 | Retrieval-Augmented Reinforcement Learning. Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter Conway Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell |
| 2022 | RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval. Yihan Wu, Hongyang Zhang, Heng Huang |
| 2022 | Retroformer: Pushing the Limits of End-to-end Retrosynthesis Transformer. Yue Wan, Chang-Yu Hsieh, Ben Liao, Shengyu Zhang |
| 2022 | Reverse Engineering the Neural Tangent Kernel. James Benjamin Simon, Sajant Anand, Michael Robert DeWeese |
| 2022 | Reverse Engineering ℓ Darshan Thaker, Paris Giampouras, René Vidal |
| 2022 | Revisiting Consistency Regularization for Deep Partial Label Learning. Dong-Dong Wu, Deng-Bao Wang, Min-Ling Zhang |
| 2022 | Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework. Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, Lily Weng |
| 2022 | Revisiting End-to-End Speech-to-Text Translation From Scratch. Biao Zhang, Barry Haddow, Rico Sennrich |
| 2022 | Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing? Keshigeyan Chandrasegaran, Ngoc-Trung Tran, Yunqing Zhao, Ngai-Man Cheung |
| 2022 | Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness. Shinji Ito |
| 2022 | Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning. Wei Fu, Chao Yu, Zelai Xu, Jiaqi Yang, Yi Wu |
| 2022 | Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization. Yihua Zhang, Guanhua Zhang, Prashant Khanduri, Mingyi Hong, Shiyu Chang, Sijia Liu |
| 2022 | Revisiting the Effects of Stochasticity for Hamiltonian Samplers. Giulio Franzese, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi |
| 2022 | Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes. Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson |
| 2022 | Rich Feature Construction for the Optimization-Generalization Dilemma. Jianyu Zhang, David Lopez-Paz, Léon Bottou |
| 2022 | RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests. Victor Chernozhukov, Whitney Newey, Victor Quintas-Martinez, Vasilis Syrgkanis |
| 2022 | Ripple Attention for Visual Perception with Sub-quadratic Complexity. Lin Zheng, Huijie Pan, Lingpeng Kong |
| 2022 | Risk-Averse No-Regret Learning in Online Convex Games. Zifan Wang, Yi Shen, Michael M. Zavlanos |
| 2022 | Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data. Georgi Ganev, Bristena Oprisanu, Emiliano De Cristofaro |
| 2022 | Robust Counterfactual Explanations for Tree-Based Ensembles. Sanghamitra Dutta, Jason Long, Saumitra Mishra, Cecilia Tilli, Daniele Magazzeni |
| 2022 | Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum. Junlin Wu, Yevgeniy Vorobeychik |
| 2022 | Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees. Haotian Ju, Dongyue Li, Hongyang R. Zhang |
| 2022 | Robust Group Synchronization via Quadratic Programming. Yunpeng Shi, Cole M. Wyeth, Gilad Lerman |
| 2022 | Robust Imitation Learning against Variations in Environment Dynamics. Jongseong Chae, Seungyul Han, Whiyoung Jung, Myungsik Cho, Sungho Choi, Youngchul Sung |
| 2022 | Robust Kernel Density Estimation with Median-of-Means principle. Pierre Humbert, Batiste Le Bars, Ludovic Minvielle |
| 2022 | Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile. Dong Chen, Lingfei Wu, Siliang Tang, Xiao Yun, Bo Long, Yueting Zhuang |
| 2022 | Robust Models Are More Interpretable Because Attributions Look Normal. Zifan Wang, Matt Fredrikson, Anupam Datta |
| 2022 | Robust Multi-Objective Bayesian Optimization Under Input Noise. Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy |
| 2022 | Robust Policy Learning over Multiple Uncertainty Sets. Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, Amy Zhang |
| 2022 | Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning. Lorenz Richter, Julius Berner |
| 2022 | Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning. Haoqi Yuan, Zongqing Lu |
| 2022 | Robust Training of Neural Networks Using Scale Invariant Architectures. Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank J. Reddi, Sanjiv Kumar |
| 2022 | Robust Training under Label Noise by Over-parameterization. Sheng Liu, Zhihui Zhu, Qing Qu, Chong You |
| 2022 | Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation. Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig |
| 2022 | Robustness Implies Generalization via Data-Dependent Generalization Bounds. Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang |
| 2022 | Robustness Verification for Contrastive Learning. Zekai Wang, Weiwei Liu |
| 2022 | Robustness and Accuracy Could Be Reconcilable by (Proper) Definition. Tianyu Pang, Min Lin, Xiao Yang, Jun Zhu, Shuicheng Yan |
| 2022 | Robustness in Multi-Objective Submodular Optimization: a Quantile Approach. Cédric Malherbe, Kevin Scaman |
| 2022 | Role-based Multiplex Network Embedding. Hegui Zhang, Gang Kou |
| 2022 | Rotting Infinitely Many-Armed Bandits. Jung-Hun Kim, Milan Vojnovic, Se-Young Yun |
| 2022 | SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation. Giorgio Giannone, Ole Winther |
| 2022 | SDQ: Stochastic Differentiable Quantization with Mixed Precision. Xijie Huang, Zhiqiang Shen, Shichao Li, Zechun Liu, Xianghong Hu, Jeffry Wicaksana, Eric P. Xing, Kwang-Ting Cheng |
| 2022 | SE(3) Equivariant Graph Neural Networks with Complete Local Frames. Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Nanning Zheng, Bin Shao, Tie-Yan Liu |
| 2022 | SPDY: Accurate Pruning with Speedup Guarantees. Elias Frantar, Dan Alistarh |
| 2022 | SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators. Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer |
| 2022 | SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization. Yuhta Takida, Takashi Shibuya, Wei-Hsiang Liao, Chieh-Hsin Lai, Junki Ohmura, Toshimitsu Uesaka, Naoki Murata, Shusuke Takahashi, Toshiyuki Kumakura, Yuki Mitsufuji |
| 2022 | SSL Enables Learning from Sparse Rewards in Image-Goal Navigation. Arjun Majumdar, Gunnar A. Sigurdsson, Robinson Piramuthu, Jesse Thomason, Dhruv Batra, Gaurav S. Sukhatme |
| 2022 | Safe Exploration for Efficient Policy Evaluation and Comparison. Runzhe Wan, Branislav Kveton, Rui Song |
| 2022 | Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints. Martino Bernasconi, Federico Cacciamani, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti, Francesco Trovò |
| 2022 | Sample Efficient Learning of Predictors that Complement Humans. Mohammad-Amin Charusaie, Hussein Mozannar, David A. Sontag, Samira Samadi |
| 2022 | Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis. Ziyi Chen, Yi Zhou, Rong-Rong Chen, Shaofeng Zou |
| 2022 | Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost. Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang |
| 2022 | Sanity Simulations for Saliency Methods. Joon Sik Kim, Gregory Plumb, Ameet Talwalkar |
| 2022 | Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation. Aivar Sootla, Alexander I. Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David Henry Mguni, Jun Wang, Haitham Ammar |
| 2022 | Scalable Computation of Causal Bounds. Madhumitha Shridharan, Garud Iyengar |
| 2022 | Scalable Deep Gaussian Markov Random Fields for General Graphs. Joel Oskarsson, Per Sidén, Fredrik Lindsten |
| 2022 | Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. Mathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Elie, Olivier Pietquin, Matthieu Geist |
| 2022 | Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation. Sebastian E. Ament, Carla P. Gomes |
| 2022 | Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes. Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi |
| 2022 | Scalable Spike-and-Slab. Niloy Biswas, Lester Mackey, Xiao-Li Meng |
| 2022 | Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco |
| 2022 | Scaling Out-of-Distribution Detection for Real-World Settings. Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, Dawn Song |
| 2022 | Scaling Structured Inference with Randomization. Yao Fu, John P. Cunningham, Mirella Lapata |
| 2022 | Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework. Jiahao Su, Wonmin Byeon, Furong Huang |
| 2022 | Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models. Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello |
| 2022 | Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems. Giannis Daras, Yuval Dagan, Alex Dimakis, Constantinos Daskalakis |
| 2022 | Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations. Jaehyeong Jo, Seul Lee, Sung Ju Hwang |
| 2022 | Searching for BurgerFormer with Micro-Meso-Macro Space Design. Longxing Yang, Yu Hu, Shun Lu, Zihao Sun, Jilin Mei, Yinhe Han, Xiaowei Li |
| 2022 | Secure Distributed Training at Scale. Eduard Gorbunov, Alexander Borzunov, Michael Diskin, Max Ryabinin |
| 2022 | Secure Quantized Training for Deep Learning. Marcel Keller, Ke Sun |
| 2022 | Selective Network Linearization for Efficient Private Inference. Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, Chinmay Hegde |
| 2022 | Selective Regression under Fairness Criteria. Abhin Shah, Yuheng Bu, Joshua K. Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W. Wornell |
| 2022 | Self-Organized Polynomial-Time Coordination Graphs. Qianlan Yang, Weijun Dong, Zhizhou Ren, Jianhao Wang, Tonghan Wang, Chongjie Zhang |
| 2022 | Self-Supervised Models of Audio Effectively Explain Human Cortical Responses to Speech. Aditya R. Vaidya, Shailee Jain, Alexander Huth |
| 2022 | Self-Supervised Representation Learning via Latent Graph Prediction. Yaochen Xie, Zhao Xu, Shuiwang Ji |
| 2022 | Self-conditioning Pre-Trained Language Models. Xavier Suau Cuadros, Luca Zappella, Nicholas Apostoloff |
| 2022 | Self-supervised Models are Good Teaching Assistants for Vision Transformers. Haiyan Wu, Yuting Gao, Yinqi Zhang, Shaohui Lin, Yuan Xie, Xing Sun, Ke Li |
| 2022 | Self-supervised learning with random-projection quantizer for speech recognition. Chung-Cheng Chiu, James Qin, Yu Zhang, Jiahui Yu, Yonghui Wu |
| 2022 | Selling Data To a Machine Learner: Pricing via Costly Signaling. Junjie Chen, Minming Li, Haifeng Xu |
| 2022 | Sequential Covariate Shift Detection Using Classifier Two-Sample Tests. Sooyong Jang, Sangdon Park, Insup Lee, Osbert Bastani |
| 2022 | Sequential and Parallel Constrained Max-value Entropy Search via Information Lower Bound. Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama |
| 2022 | Set Based Stochastic Subsampling. Bruno Andreis, Seanie Lee, Tuan A. Nguyen, Juho Lee, Eunho Yang, Sung Ju Hwang |
| 2022 | Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets. Lily H. Zhang, Veronica Tozzo, John M. Higgins, Rajesh Ranganath |
| 2022 | Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning. Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, Tianyi Chen |
| 2022 | Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood. Qiujiang Jin, Alec Koppel, Ketan Rajawat, Aryan Mokhtari |
| 2022 | ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks. Haoran You, Baopu Li, Huihong Shi, Yonggan Fu, Yingyan Lin |
| 2022 | Short-Term Plasticity Neurons Learning to Learn and Forget. Hector Garcia Rodriguez, Qinghai Guo, Timoleon Moraitis |
| 2022 | Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters. Vladislav Kurenkov, Sergey Kolesnikov |
| 2022 | Shuffle Private Linear Contextual Bandits. Sayak Ray Chowdhury, Xingyu Zhou |
| 2022 | Simple and near-optimal algorithms for hidden stratification and multi-group learning. Christopher J. Tosh, Daniel Hsu |
| 2022 | Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games. Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess |
| 2022 | Simultaneous Graph Signal Clustering and Graph Learning. Abdullah Karaaslanli, Selin Aviyente |
| 2022 | Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback. Fang Kong, Yichi Zhou, Shuai Li |
| 2022 | Sketching Algorithms and Lower Bounds for Ridge Regression. Praneeth Kacham, David P. Woodruff |
| 2022 | SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks. Xiang Xu, Karl D. D. Willis, Joseph G. Lambourne, Chin-Yi Cheng, Pradeep Kumar Jayaraman, Yasutaka Furukawa |
| 2022 | Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification. Peter J. Bevan, Amir Atapour-Abarghouei |
| 2022 | Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data. Zhengfeng Lai, Chao Wang, Henrry Gunawan, Sen-Ching S. Cheung, Chen-Nee Chuah |
| 2022 | Smoothed Adversarial Linear Contextual Bandits with Knapsacks. Vidyashankar Sivakumar, Shiliang Zuo, Arindam Banerjee |
| 2022 | SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals. Dani Kiyasseh, Tingting Zhu, David A. Clifton |
| 2022 | Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation. Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, Il-Chul Moon |
| 2022 | Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation. Jiali Wang, Wen Huang, Rujun Jiang, Xudong Li, Alex L. Wang |
| 2022 | SpaceMAP: Visualizing High-Dimensional Data by Space Expansion. Xinrui Zu, Qian Tao |
| 2022 | Sparse Double Descent: Where Network Pruning Aggravates Overfitting. Zheng He, Zeke Xie, Quanzhi Zhu, Zengchang Qin |
| 2022 | Sparse Invariant Risk Minimization. Xiao Zhou, Yong Lin, Weizhong Zhang, Tong Zhang |
| 2022 | Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation. Adarsh Barik, Jean Honorio |
| 2022 | Sparsity in Partially Controllable Linear Systems. Yonathan Efroni, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang |
| 2022 | Spatial-Channel Token Distillation for Vision MLPs. Yanxi Li, Xinghao Chen, Minjing Dong, Yehui Tang, Yunhe Wang, Chang Xu |
| 2022 | Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty. Jixiang Qing, Tom Dhaene, Ivo Couckuyt |
| 2022 | SpeqNets: Sparsity-aware permutation-equivariant graph networks. Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh |
| 2022 | Stability Based Generalization Bounds for Exponential Family Langevin Dynamics. Arindam Banerjee, Tiancong Chen, Xinyan Li, Yingxue Zhou |
| 2022 | Stabilizing Off-Policy Deep Reinforcement Learning from Pixels. Edoardo Cetin, Philip J. Ball, Stephen J. Roberts, Oya Çeliktutan |
| 2022 | Stabilizing Q-learning with Linear Architectures for Provable Efficient Learning. Andrea Zanette, Martin J. Wainwright |
| 2022 | Stable Conformal Prediction Sets. Eugène Ndiaye |
| 2022 | Staged Training for Transformer Language Models. Sheng Shen, Pete Walsh, Kurt Keutzer, Jesse Dodge, Matthew E. Peters, Iz Beltagy |
| 2022 | State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks. Yanqi Chen, Zhaofei Yu, Wei Fang, Zhengyu Ma, Tiejun Huang, Yonghong Tian |
| 2022 | Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert. Yoonhyung Lee, Sungdong Lee, Joong-Ho Won |
| 2022 | Steerable 3D Spherical Neurons. Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck |
| 2022 | Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier |
| 2022 | Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function. Qixin Zhang, Zengde Deng, Zaiyi Chen, Haoyuan Hu, Yu Yang |
| 2022 | Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning. Konstantinos Kalais, Sotirios Chatzis |
| 2022 | Stochastic Reweighted Gradient Descent. Ayoub El Hanchi, David A. Stephens, Chris J. Maddison |
| 2022 | Stochastic Rising Bandits. Alberto Maria Metelli, Francesco Trovò, Matteo Pirola, Marcello Restelli |
| 2022 | Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification. Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon |
| 2022 | Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses. Keegan Harris, Dung Daniel T. Ngo, Logan Stapleton, Hoda Heidari, Steven Wu |
| 2022 | Strategic Representation. Vineet Nair, Ganesh Ghalme, Inbal Talgam-Cohen, Nir Rosenfeld |
| 2022 | Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk. Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama |
| 2022 | Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints. Alina Ene, Huy L. Nguyen |
| 2022 | Streaming Algorithms for High-Dimensional Robust Statistics. Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas |
| 2022 | Streaming Algorithms for Support-Aware Histograms. Justin Y. Chen, Piotr Indyk, Tal Wagner |
| 2022 | Streaming Inference for Infinite Feature Models. Rylan Schaeffer, Yilun Du, Gabrielle K. Liu, Ila Fiete |
| 2022 | StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models. Adam Liska, Tomás Kociský, Elena Gribovskaya, Tayfun Terzi, Eren Sezener, Devang Agrawal, Cyprien de Masson d'Autume, Tim Scholtes, Manzil Zaheer, Susannah Young, Ellen Gilsenan-McMahon, Sophia Austin, Phil Blunsom, Angeliki Lazaridou |
| 2022 | Structural Entropy Guided Graph Hierarchical Pooling. Junran Wu, Xueyuan Chen, Ke Xu, Shangzhe Li |
| 2022 | Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation. Steffen Schotthöfer, Tianbai Xiao, Martin Frank, Cory D. Hauck |
| 2022 | Structure-Aware Transformer for Graph Representation Learning. Dexiong Chen, Leslie O'Bray, Karsten M. Borgwardt |
| 2022 | Structure-preserving GANs. Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu |
| 2022 | Structured Stochastic Gradient MCMC. Antonios Alexos, Alex J. Boyd, Stephan Mandt |
| 2022 | Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models. Jen-Hao Rick Chang, Ashish Shrivastava, Hema Koppula, Xiaoshuai Zhang, Oncel Tuzel |
| 2022 | Sublinear-Time Clustering Oracle for Signed Graphs. Stefan Neumann, Pan Peng |
| 2022 | Subspace Learning for Effective Meta-Learning. Weisen Jiang, James T. Kwok, Yu Zhang |
| 2022 | Supervised Learning with General Risk Functionals. Liu Leqi, Audrey Huang, Zachary C. Lipton, Kamyar Azizzadenesheli |
| 2022 | Supervised Off-Policy Ranking. Yue Jin, Yue Zhang, Tao Qin, Xudong Zhang, Jian Yuan, Houqiang Li, Tie-Yan Liu |
| 2022 | Surrogate Likelihoods for Variational Annealed Importance Sampling. Martin Jankowiak, Du Phan |
| 2022 | Symmetric Machine Theory of Mind. Melanie Sclar, Graham Neubig, Yonatan Bisk |
| 2022 | Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm. Lechao Xiao, Jeffrey Pennington |
| 2022 | TACTiS: Transformer-Attentional Copulas for Time Series. Alexandre Drouin, Étienne Marcotte, Nicolas Chapados |
| 2022 | TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification. Jaeyun Song, Joonhyung Park, Eunho Yang |
| 2022 | TPC: Transformation-Specific Smoothing for Point Cloud Models. Wenda Chu, Linyi Li, Bo Li |
| 2022 | TSPipe: Learn from Teacher Faster with Pipelines. Hwijoon Lim, Yechan Kim, Sukmin Yun, Jinwoo Shin, Dongsu Han |
| 2022 | TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm. Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar |
| 2022 | Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology. Yan Huang, Ying Sun, Zehan Zhu, Changzhi Yan, Jinming Xu |
| 2022 | Tackling covariate shift with node-based Bayesian neural networks. Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski |
| 2022 | Task-aware Privacy Preservation for Multi-dimensional Data. Jiangnan Cheng, Ao Tang, Sandeep Chinchali |
| 2022 | Tell me why! Explanations support learning relational and causal structure. Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta, Stephanie C. Y. Chan, Allison C. Tam, James L. McClelland, Chen Yan, Adam Santoro, Neil C. Rabinowitz, Jane X. Wang, Felix Hill |
| 2022 | Temporal Difference Learning for Model Predictive Control. Nicklas Hansen, Hao Su, Xiaolong Wang |
| 2022 | Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing. Mohammad Zalbagi Darestani, Jiayu Liu, Reinhard Heckel |
| 2022 | The Algebraic Path Problem for Graph Metrics. Enrique Fita Sanmartín, Sebastian Damrich, Fred A. Hamprecht |
| 2022 | The CLRS Algorithmic Reasoning Benchmark. Petar Velickovic, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, Charles Blundell |
| 2022 | The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks. Xin Yu, Thiago Serra, Srikumar Ramalingam, Shandian Zhe |
| 2022 | The Complexity of k-Means Clustering when Little is Known. Robert Ganian, Thekla Hamm, Viktoriia Korchemna, Karolina Okrasa, Kirill Simonov |
| 2022 | The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention. Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber |
| 2022 | The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning. Wei-Ning Chen, Christopher A. Choquette-Choo, Peter Kairouz, Ananda Theertha Suresh |
| 2022 | The Geometry of Robust Value Functions. Kaixin Wang, Navdeep Kumar, Kuangqi Zhou, Bryan Hooi, Jiashi Feng, Shie Mannor |
| 2022 | The Importance of Non-Markovianity in Maximum State Entropy Exploration. Mirco Mutti, Riccardo De Santi, Marcello Restelli |
| 2022 | The Infinite Contextual Graph Markov Model. Daniele Castellana, Federico Errica, Davide Bacciu, Alessio Micheli |
| 2022 | The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks. Hadeel Soliman, Lingfei Zhao, Zhipeng Huang, Subhadeep Paul, Kevin S. Xu |
| 2022 | The Neural Race Reduction: Dynamics of Abstraction in Gated Networks. Andrew M. Saxe, Shagun Sodhani, Sam Jay Lewallen |
| 2022 | The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation. Wei-Ning Chen, Ayfer Özgür, Peter Kairouz |
| 2022 | The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces. Chi Jin, Qinghua Liu, Tiancheng Yu |
| 2022 | The Primacy Bias in Deep Reinforcement Learning. Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron C. Courville |
| 2022 | The Role of Deconfounding in Meta-learning. Yinjie Jiang, Zhengyu Chen, Kun Kuang, Luotian Yuan, Xinhai Ye, Zhihua Wang, Fei Wu, Ying Wei |
| 2022 | The State of Sparse Training in Deep Reinforcement Learning. Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro |
| 2022 | The Teaching Dimension of Regularized Kernel Learners. Hong Qian, Xu-Hui Liu, Chen-Xi Su, Aimin Zhou, Yang Yu |
| 2022 | The Unsurprising Effectiveness of Pre-Trained Vision Models for Control. Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta |
| 2022 | The dynamics of representation learning in shallow, non-linear autoencoders. Maria Refinetti, Sebastian Goldt |
| 2022 | The power of first-order smooth optimization for black-box non-smooth problems. Alexander V. Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov, Aleksandr Beznosikov, Martin Takác, Pavel E. Dvurechensky, Bin Gu |
| 2022 | Thompson Sampling for (Combinatorial) Pure Exploration. Siwei Wang, Jun Zhu |
| 2022 | Thompson Sampling for Robust Transfer in Multi-Task Bandits. Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri |
| 2022 | Three-stage Evolution and Fast Equilibrium for SGD with Non-degerate Critical Points. Yi Wang, Zhiren Wang |
| 2022 | Thresholded Lasso Bandit. Kaito Ariu, Kenshi Abe, Alexandre Proutière |
| 2022 | Tight and Robust Private Mean Estimation with Few Users. Shyam Narayanan, Vahab S. Mirrokni, Hossein Esfandiari |
| 2022 | Time Is MattEr: Temporal Self-supervision for Video Transformers. Sukmin Yun, Jaehyung Kim, Dongyoon Han, Hwanjun Song, Jung-Woo Ha, Jinwoo Shin |
| 2022 | To Smooth or Not? When Label Smoothing Meets Noisy Labels. Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu |
| 2022 | Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning. Sixing Yu, Arya Mazaheri, Ali Jannesari |
| 2022 | Topology-aware Generalization of Decentralized SGD. Tongtian Zhu, Fengxiang He, Lan Zhang, Zhengyang Niu, Mingli Song, Dacheng Tao |
| 2022 | Toward Compositional Generalization in Object-Oriented World Modeling. Linfeng Zhao, Lingzhi Kong, Robin Walters, Lawson L. S. Wong |
| 2022 | Towards Coherent and Consistent Use of Entities in Narrative Generation. Pinelopi Papalampidi, Kris Cao, Tomás Kociský |
| 2022 | Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods. Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm van Seijen |
| 2022 | Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent. Sharan Vaswani, Benjamin Dubois-Taine, Reza Babanezhad |
| 2022 | Towards Scaling Difference Target Propagation by Learning Backprop Targets. Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake A. Richards, Yoshua Bengio |
| 2022 | Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs. Jie Ren, Mingjie Li, Meng Zhou, Shih-Han Chan, Quanshi Zhang |
| 2022 | Towards Understanding Sharpness-Aware Minimization. Maksym Andriushchenko, Nicolas Flammarion |
| 2022 | Towards Uniformly Superhuman Autonomy via Subdominance Minimization. Brian D. Ziebart, Sanjiban Choudhury, Xinyan Yan, Paul Vernaza |
| 2022 | Towards understanding how momentum improves generalization in deep learning. Samy Jelassi, Yuanzhi Li |
| 2022 | Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems. Manuel Brenner, Florian Hess, Jonas M. Mikhaeil, Leonard F. Bereska, Zahra Monfared, Po-Chen Kuo, Daniel Durstewitz |
| 2022 | Tractable Uncertainty for Structure Learning. Benjie Wang, Matthew Wicker, Marta Kwiatkowska |
| 2022 | Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four. Stephan Wäldchen, Sebastian Pokutta, Felix Huber |
| 2022 | Training Discrete Deep Generative Models via Gapped Straight-Through Estimator. Ting-Han Fan, Ta-Chung Chi, Alexander I. Rudnicky, Peter J. Ramadge |
| 2022 | Training OOD Detectors in their Natural Habitats. Julian Katz-Samuels, Julia B. Nakhleh, Robert D. Nowak, Yixuan Li |
| 2022 | Training Your Sparse Neural Network Better with Any Mask. Ajay Kumar Jaiswal, Haoyu Ma, Tianlong Chen, Ying Ding, Zhangyang Wang |
| 2022 | Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N. Gomez, Debora S. Marks, Yarin Gal |
| 2022 | Transfer Learning In Differential Privacy's Hybrid-Model. Refael Kohen, Or Sheffet |
| 2022 | Transfer and Marginalize: Explaining Away Label Noise with Privileged Information. Mark Collier, Rodolphe Jenatton, Effrosyni Kokiopoulou, Jesse Berent |
| 2022 | Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling. Tung Nguyen, Aditya Grover |
| 2022 | Transformer Quality in Linear Time. Weizhe Hua, Zihang Dai, Hanxiao Liu, Quoc V. Le |
| 2022 | Transformers are Meta-Reinforcement Learners. Luckeciano C. Melo |
| 2022 | Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots. Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh |
| 2022 | Translatotron 2: High-quality direct speech-to-speech translation with voice preservation. Ye Jia, Michelle Tadmor Ramanovich, Tal Remez, Roi Pomerantz |
| 2022 | UAST: Uncertainty-Aware Siamese Tracking. Dawei Zhang, Yanwei Fu, Zhonglong Zheng |
| 2022 | UNIREX: A Unified Learning Framework for Language Model Rationale Extraction. Aaron Chan, Maziar Sanjabi, Lambert Mathias, Liang Tan, Shaoliang Nie, Xiaochang Peng, Xiang Ren, Hamed Firooz |
| 2022 | Unaligned Supervision for Automatic Music Transcription in The Wild. Ben Maman, Amit H. Bermano |
| 2022 | Uncertainty Modeling in Generative Compressed Sensing. Yilang Zhang, Mengchu Xu, Xiaojun Mao, Jian Wang |
| 2022 | UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees. Kimon Antonakopoulos, Dong Quan Vu, Volkan Cevher, Kfir Y. Levy, Panayotis Mertikopoulos |
| 2022 | Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy. Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Steven Wu, Jinfeng Yi |
| 2022 | Understanding Contrastive Learning Requires Incorporating Inductive Biases. Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy |
| 2022 | Understanding Dataset Difficulty with Kawin Ethayarajh, Yejin Choi, Swabha Swayamdipta |
| 2022 | Understanding Doubly Stochastic Clustering. Tianjiao Ding, Derek Lim, René Vidal, Benjamin D. Haeffele |
| 2022 | Understanding Gradient Descent on the Edge of Stability in Deep Learning. Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi |
| 2022 | Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond. Haoxiang Wang, Bo Li, Han Zhao |
| 2022 | Understanding Instance-Level Impact of Fairness Constraints. Jialu Wang, Xin Eric Wang, Yang Liu |
| 2022 | Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach. Shuang Wu, Ling Shi, Jun Wang, Guangjian Tian |
| 2022 | Understanding Robust Generalization in Learning Regular Languages. Soham Dan, Osbert Bastani, Dan Roth |
| 2022 | Understanding Robust Overfitting of Adversarial Training and Beyond. Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu |
| 2022 | Understanding The Robustness in Vision Transformers. Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Animashree Anandkumar, Jiashi Feng, José M. Álvarez |
| 2022 | Understanding and Improving Knowledge Graph Embedding for Entity Alignment. Lingbing Guo, Qiang Zhang, Zequn Sun, Mingyang Chen, Wei Hu, Huajun Chen |
| 2022 | Understanding the unstable convergence of gradient descent. Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra |
| 2022 | UniRank: Unimodal Bandit Algorithms for Online Ranking. Camille-Sovanneary Gauthier, Romaric Gaudel, Élisa Fromont |
| 2022 | Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces. Yinshuang Xu, Jiahui Lei, Edgar Dobriban, Kostas Daniilidis |
| 2022 | Unified Scaling Laws for Routed Language Models. Aidan Clark, Diego de Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake A. Hechtman, Trevor Cai, Sebastian Borgeaud, George van den Driessche, Eliza Rutherford, Tom Hennigan, Matthew J. Johnson, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, Laurent Sifre, Simon Osindero, Oriol Vinyals, Marc'Aurelio Ranzato, Jack W. Rae, Erich Elsen, Koray Kavukcuoglu, Karen Simonyan |
| 2022 | Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models. Beren Millidge, Tommaso Salvatori, Yuhang Song, Thomas Lukasiewicz, Rafal Bogacz |
| 2022 | Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows. Michael Puthawala, Matti Lassas, Ivan Dokmanic, Maarten V. de Hoop |
| 2022 | Universal and data-adaptive algorithms for model selection in linear contextual bandits. Vidya K. Muthukumar, Akshay Krishnamurthy |
| 2022 | Universality of Winning Tickets: A Renormalization Group Perspective. William T. Redman, Tianlong Chen, Zhangyang Wang, Akshunna S. Dogra |
| 2022 | Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers. Arda Sahiner, Tolga Ergen, Batu Ozturkler, John M. Pauly, Morteza Mardani, Mert Pilanci |
| 2022 | Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology. Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze |
| 2022 | Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration. Jing Lin, Xiaowan Hu, Yuanhao Cai, Haoqian Wang, Youliang Yan, Xueyi Zou, Yulun Zhang, Luc Van Gool |
| 2022 | Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors. Geert-Jan Huizing, Laura Cantini, Gabriel Peyré |
| 2022 | Unsupervised Image Representation Learning with Deep Latent Particles. Tal Daniel, Aviv Tamar |
| 2022 | Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion. Ling Yang, Shenda Hong |
| 2022 | Utility Theory for Sequential Decision Making. Mehran Shakerinava, Siamak Ravanbakhsh |
| 2022 | Utilizing Expert Features for Contrastive Learning of Time-Series Representations. Manuel T. Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb |
| 2022 | VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix. Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo |
| 2022 | VLUE: A Multi-Task Multi-Dimension Benchmark for Evaluating Vision-Language Pre-training. Wangchunshu Zhou, Yan Zeng, Shizhe Diao, Xinsong Zhang |
| 2022 | Validating Causal Inference Methods. Harsh Parikh, Carlos Varjao, Louise Xu, Eric Tchetgen Tchetgen |
| 2022 | Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems. Lucy L. Gao, Jane J. Ye, Haian Yin, Shangzhi Zeng, Jin Zhang |
| 2022 | VarScene: A Deep Generative Model for Realistic Scene Graph Synthesis. Tathagat Verma, Abir De, Yateesh Agrawal, Vishwa Vinay, Soumen Chakrabarti |
| 2022 | VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty. Randy Ardywibowo, Zepeng Huo, Zhangyang Wang, Bobak J. Mortazavi, Shuai Huang, Xiaoning Qian |
| 2022 | Variational Feature Pyramid Networks. Panagiotis Dimitrakopoulos, Giorgos Sfikas, Christophoros Nikou |
| 2022 | Variational Inference for Infinitely Deep Neural Networks. Achille Nazaret, David M. Blei |
| 2022 | Variational Inference with Locally Enhanced Bounds for Hierarchical Models. Tomas Geffner, Justin Domke |
| 2022 | Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics. Yichen Gu, David T. Blaauw, Joshua D. Welch |
| 2022 | Variational On-the-Fly Personalization. Jangho Kim, Juntae Lee, Simyung Chang, Nojun Kwak |
| 2022 | Variational Sparse Coding with Learned Thresholding. Kion Fallah, Christopher J. Rozell |
| 2022 | Variational Wasserstein gradient flow. Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei, Yongxin Chen |
| 2022 | Variational nearest neighbor Gaussian process. Luhuan Wu, Geoff Pleiss, John P. Cunningham |
| 2022 | Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences. Aadirupa Saha, Pierre Gaillard |
| 2022 | Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching. Yecheng Jason Ma, Andrew Shen, Dinesh Jayaraman, Osbert Bastani |
| 2022 | ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder. Sangwon Kim, Jae-Yeal Nam, ByoungChul Ko |
| 2022 | Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xin He, Bo Han, Xiaowen Chu |
| 2022 | Visual Attention Emerges from Recurrent Sparse Reconstruction. Baifeng Shi, Yale Song, Neel Joshi, Trevor Darrell, Xin Wang |
| 2022 | Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes. Gregory W. Benton, Wesley J. Maddox, Andrew Gordon Wilson |
| 2022 | Weisfeiler-Lehman Meets Gromov-Wasserstein. Samantha Chen, Sunhyuk Lim, Facundo Mémoli, Zhengchao Wan, Yusu Wang |
| 2022 | Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy. Zhihan Liu, Miao Lu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang |
| 2022 | What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us? Tiffany J. Vlaar, Jonathan Frankle |
| 2022 | What Dense Graph Do You Need for Self-Attention? Yuxin Wang, Chu-Tak Lee, Qipeng Guo, Zhangyue Yin, Yunhua Zhou, Xuanjing Huang, Xipeng Qiu |
| 2022 | What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization? Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, Colin Raffel |
| 2022 | When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee. Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang |
| 2022 | When Are Linear Stochastic Bandits Attackable? Huazheng Wang, Haifeng Xu, Hongning Wang |
| 2022 | When and How Mixup Improves Calibration. Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou |
| 2022 | Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error. Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu |
| 2022 | Why the Rich Get Richer? On the Balancedness of Random Partition Models. Changwoo J. Lee, Huiyan Sang |
| 2022 | Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling. Jiri Hron, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein |
| 2022 | Wide Neural Networks Forget Less Catastrophically. Seyed-Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Huiyi Hu, Razvan Pascanu, Dilan Görür, Mehrdad Farajtabar |
| 2022 | Winning the Lottery Ahead of Time: Efficient Early Network Pruning. John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann |
| 2022 | XAI for Transformers: Better Explanations through Conservative Propagation. Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf |
| 2022 | You Only Cut Once: Boosting Data Augmentation with a Single Cut. Junlin Han, Pengfei Fang, Weihao Li, Jie Hong, Mohammad Ali Armin, Ian D. Reid, Lars Petersson, Hongdong Li |
| 2022 | YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone. Edresson Casanova, Julian Weber, Christopher Dane Shulby, Arnaldo Cândido Júnior, Eren Gölge, Moacir A. Ponti |
| 2022 | Zero-Shot Reward Specification via Grounded Natural Language. Parsa Mahmoudieh, Deepak Pathak, Trevor Darrell |
| 2022 | Zero-shot AutoML with Pretrained Models. Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter |
| 2022 | data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language. Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli |
| 2022 | p-Laplacian Based Graph Neural Networks. Guoji Fu, Peilin Zhao, Yatao Bian |
| 2022 | pathGCN: Learning General Graph Spatial Operators from Paths. Moshe Eliasof, Eldad Haber, Eran Treister |