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| 2021 | A Change of Variables Method For Rectangular Matrix-Vector Products. Edmond Cunningham, Madalina Fiterau |
| 2021 | A Contraction Approach to Model-based Reinforcement Learning. Ting-Han Fan, Peter J. Ramadge |
| 2021 | A Deterministic Streaming Sketch for Ridge Regression. Benwei Shi, Jeff M. Phillips |
| 2021 | A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks. Zhiqi Bu, Shiyun Xu, Kan Chen |
| 2021 | A Fast and Robust Method for Global Topological Functional Optimization. Elchanan Solomon, Alexander Wagner, Paul Bendich |
| 2021 | A Hybrid Approximation to the Marginal Likelihood. Eric Chuu, Debdeep Pati, Anirban Bhattacharya |
| 2021 | A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko |
| 2021 | A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets. Gauthier Gidel, David Balduzzi, Wojciech Czarnecki, Marta Garnelo, Yoram Bachrach |
| 2021 | A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich |
| 2021 | A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits. Kei Takemura, Shinji Ito, Daisuke Hatano, Hanna Sumita, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi |
| 2021 | A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models. Jiaxin Zhang, Sirui Bi, Guannan Zhang |
| 2021 | A Spectral Analysis of Dot-product Kernels. Meyer Scetbon, Zaïd Harchaoui |
| 2021 | A Statistical Perspective on Coreset Density Estimation. Paxton Turner, Jingbo Liu, Philippe Rigollet |
| 2021 | A Stein Goodness-of-test for Exponential Random Graph Models. Wenkai Xu, Gesine Reinert |
| 2021 | A Study of Condition Numbers for First-Order Optimization. Charles Guille-Escuret, Manuela Girotti, Baptiste Goujaud, Ioannis Mitliagkas |
| 2021 | A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix. Thang Doan, Mehdi Abbana Bennani, Bogdan Mazoure, Guillaume Rabusseau, Pierre Alquier |
| 2021 | A Theoretical Characterization of Semi-supervised Learning with Self-training for Gaussian Mixture Models. Samet Oymak, Talha Cihad Gulcu |
| 2021 | A Theory of Multiple-Source Adaptation with Limited Target Labeled Data. Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh, Ke Wu |
| 2021 | A Variational Inference Approach to Learning Multivariate Wold Processes. Jalal Etesami, William Trouleau, Negar Kiyavash, Matthias Grossglauser, Patrick Thiran |
| 2021 | A Variational Information Bottleneck Approach to Multi-Omics Data Integration. Changhee Lee, Mihaela van der Schaar |
| 2021 | A comparative study on sampling with replacement vs Poisson sampling in optimal subsampling. HaiYing Wang, Jiahui Zou |
| 2021 | A constrained risk inequality for general losses. John C. Duchi, Feng Ruan |
| 2021 | A unified view of likelihood ratio and reparameterization gradients. Paavo Parmas, Masashi Sugiyama |
| 2021 | ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning. Martin Royer, Frédéric Chazal, Clément Levrard, Yuhei Umeda, Yuichi Ike |
| 2021 | Abstract Value Iteration for Hierarchical Reinforcement Learning. Kishor Jothimurugan, Osbert Bastani, Rajeev Alur |
| 2021 | Accelerating Metropolis-Hastings with Lightweight Inference Compilation. Feynman T. Liang, Nimar S. Arora, Nazanin Khosravani Tehrani, Yucen Lily Li, Michael Tingley, Erik Meijer |
| 2021 | Accumulations of Projections - A Unified Framework for Random Sketches in Kernel Ridge Regression. Yifan Chen, Yun Yang |
| 2021 | Active Learning under Label Shift. Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue |
| 2021 | Active Learning with Maximum Margin Sparse Gaussian Processes. Weishi Shi, Qi Yu |
| 2021 | Active Online Learning with Hidden Shifting Domains. Yining Chen, Haipeng Luo, Tengyu Ma, Chicheng Zhang |
| 2021 | Adaptive Approximate Policy Iteration. Botao Hao, Nevena Lazic, Yasin Abbasi-Yadkori, Pooria Joulani, Csaba Szepesvári |
| 2021 | Adaptive Sampling for Fast Constrained Maximization of Submodular Functions. Francesco Quinzan, Vanja Doskoc, Andreas Göbel, Tobias Friedrich |
| 2021 | Adaptive wavelet pooling for convolutional neural networks. Moritz Wolter, Jochen Garcke |
| 2021 | Adversarially Robust Estimate and Risk Analysis in Linear Regression. Yue Xing, Ruizhi Zhang, Guang Cheng |
| 2021 | Aggregating Incomplete and Noisy Rankings. Dimitris Fotakis, Alkis Kalavasis, Konstantinos Stavropoulos |
| 2021 | Algorithms for Fairness in Sequential Decision Making. Min Wen, Osbert Bastani, Ufuk Topcu |
| 2021 | Aligning Time Series on Incomparable Spaces. Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth |
| 2021 | All of the Fairness for Edge Prediction with Optimal Transport. Charlotte Laclau, Ievgen Redko, Manvi Choudhary, Christine Largeron |
| 2021 | Alternating Direction Method of Multipliers for Quantization. Tianjian Huang, Prajwal Singhania, Maziar Sanjabi, Pabitra Mitra, Meisam Razaviyayn |
| 2021 | Amortized Bayesian Prototype Meta-learning: A New Probabilistic Meta-learning Approach to Few-shot Image Classification. Zhuo Sun, Jijie Wu, Xiaoxu Li, Wenming Yang, Jing-Hao Xue |
| 2021 | An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo. Matthew Hoffman, Alexey Radul, Pavel Sountsov |
| 2021 | An Analysis of LIME for Text Data. Dina Mardaoui, Damien Garreau |
| 2021 | An Analysis of the Adaptation Speed of Causal Models. Rémi Le Priol, Reza Babanezhad, Yoshua Bengio, Simon Lacoste-Julien |
| 2021 | An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling. Qin Ding, Cho-Jui Hsieh, James Sharpnack |
| 2021 | An Optimal Reduction of TV-Denoising to Adaptive Online Learning. Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang |
| 2021 | Anderson acceleration of coordinate descent. Quentin Bertrand, Mathurin Massias |
| 2021 | Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model. Libby Zhang, Tim Dunn, Jesse Marshall, Bence Olveczky, Scott W. Linderman |
| 2021 | Approximate Data Deletion from Machine Learning Models. Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou |
| 2021 | Approximate Message Passing with Spectral Initialization for Generalized Linear Models. Marco Mondelli, Ramji Venkataramanan |
| 2021 | Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning. Kai Cui, Heinz Koeppl |
| 2021 | Approximating Lipschitz continuous functions with GroupSort neural networks. Ugo Tanielian, Gérard Biau |
| 2021 | Approximation Algorithms for Orthogonal Non-negative Matrix Factorization. Moses Charikar, Lunjia Hu |
| 2021 | Associative Convolutional Layers. Hamed Omidvar, Vahideh Akhlaghi, Hao Su, Massimo Franceschetti, Rajesh K. Gupta |
| 2021 | Asymptotics of Ridge(less) Regression under General Source Condition. Dominic Richards, Jaouad Mourtada, Lorenzo Rosasco |
| 2021 | Automatic Differentiation Variational Inference with Mixtures. Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham, Andrew G. Gallagher, Joshua V. Dillon |
| 2021 | Automatic structured variational inference. Luca Ambrogioni, Kate Lin, Emily Fertig, Sharad Vikram, Max Hinne, Dave Moore, Marcel van Gerven |
| 2021 | Bandit algorithms: Letting go of logarithmic regret for statistical robustness. Kumar Ashutosh, Jayakrishnan Nair, Anmol Kagrecha, Krishna P. Jagannathan |
| 2021 | Bayesian Active Learning by Soft Mean Objective Cost of Uncertainty. Guang Zhao, Edward R. Dougherty, Byung-Jun Yoon, Francis J. Alexander, Xiaoning Qian |
| 2021 | Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective. Jacky Zhang, Rajiv Khanna, Anastasios Kyrillidis, Sanmi Koyejo |
| 2021 | Bayesian Inference with Certifiable Adversarial Robustness. Matthew Wicker, Luca Laurenti, Andrea Patane, Zhuotong Chen, Zheng Zhang, Marta Kwiatkowska |
| 2021 | Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributions. Shunsuke Horii |
| 2021 | Benchmarking Simulation-Based Inference. Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke |
| 2021 | Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? Chaoqi Wang, Shengyang Sun, Roger B. Grosse |
| 2021 | Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances. Hunter Lang, Aravind Reddy, David A. Sontag, Aravindan Vijayaraghavan |
| 2021 | Budgeted and Non-Budgeted Causal Bandits. Vineet Nair, Vishakha Patil, Gaurav Sinha |
| 2021 | CADA: Communication-Adaptive Distributed Adam. Tianyi Chen, Ziye Guo, Yuejiao Sun, Wotao Yin |
| 2021 | CLAR: Contrastive Learning of Auditory Representations. Haider Al-Tahan, Yalda Mohsenzadeh |
| 2021 | CONTRA: Contrarian statistics for controlled variable selection. Mukund Sudarshan, Aahlad Manas Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath |
| 2021 | CWY Parametrization: a Solution for Parallelized Optimization of Orthogonal and Stiefel Matrices. Valerii Likhosherstov, Jared Davis, Krzysztof Choromanski, Adrian Weller |
| 2021 | Calibrated Adaptive Probabilistic ODE Solvers. Nathanael Bosch, Philipp Hennig, Filip Tronarp |
| 2021 | Causal Autoregressive Flows. Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen |
| 2021 | Causal Inference under Networked Interference and Intervention Policy Enhancement. Yunpu Ma, Volker Tresp |
| 2021 | Causal Inference with Selectively Deconfounded Data. Kyra Gan, Andrew A. Li, Zachary Chase Lipton, Sridhar R. Tayur |
| 2021 | Causal Modeling with Stochastic Confounders. Thanh Vinh Vo, Pengfei Wei, Wicher Bergsma, Tze-Yun Leong |
| 2021 | Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering. Sebastian Macaluso, Craig S. Greenberg, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum |
| 2021 | Clustering multilayer graphs with missing nodes. Guillaume Braun, Hemant Tyagi, Christophe Biernacki |
| 2021 | Collaborative Classification from Noisy Labels. Lucas Maystre, Nagarjuna Kumarappan, Judith Bütepage, Mounia Lalmas |
| 2021 | Combinatorial Gaussian Process Bandits with Probabilistically Triggered Arms. Ilker Demirel, Cem Tekin |
| 2021 | Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization. Congliang Chen, Jiawei Zhang, Li Shen, Peilin Zhao, Zhi-Quan Luo |
| 2021 | Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation. Mayee F. Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, Christopher Ré |
| 2021 | Competing AI: How does competition feedback affect machine learning? Tony Ginart, Eva Zhang, Yongchan Kwon, James Zou |
| 2021 | Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions. Yihan Wu, Aleksandar Bojchevski, Aleksei Kuvshinov, Stephan Günnemann |
| 2021 | Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting. Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári |
| 2021 | Consistent k-Median: Simpler, Better and Robust. Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian |
| 2021 | Context-Specific Likelihood Weighting. Nitesh Kumar, Ondrej Kuzelka |
| 2021 | Contextual Blocking Bandits. Soumya Basu, Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai |
| 2021 | Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors. Nikhil Mehta, Kevin J. Liang, Vinay Kumar Verma, Lawrence Carin |
| 2021 | Continuum-Armed Bandits: A Function Space Perspective. Shashank Singh |
| 2021 | Contrastive learning of strong-mixing continuous-time stochastic processes. Bingbin Liu, Pradeep Ravikumar, Andrej Risteski |
| 2021 | Convergence Properties of Stochastic Hypergradients. Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo |
| 2021 | Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. Zachary Charles, Jakub Konecný |
| 2021 | Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples. Yixing Zhang, Xiuyuan Cheng, Galen Reeves |
| 2021 | Corralling Stochastic Bandit Algorithms. Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri |
| 2021 | Counterfactual Representation Learning with Balancing Weights. Serge Assaad, Shuxi Zeng, Chenyang Tao, Shounak Datta, Nikhil Mehta, Ricardo Henao, Fan Li, Lawrence Carin |
| 2021 | Couplings for Multinomial Hamiltonian Monte Carlo. Kai Xu, Tor Erlend Fjelde, Charles Sutton, Hong Ge |
| 2021 | Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates. Sebastian U. Stich, Amirkeivan Mohtashami, Martin Jaggi |
| 2021 | Curriculum Learning by Optimizing Learning Dynamics. Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes |
| 2021 | DAG-Structured Clustering by Nearest Neighbors. Nicholas Monath, Manzil Zaheer, Kumar Avinava Dubey, Amr Ahmed, Andrew McCallum |
| 2021 | DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation. Frederik Harder, Kamil Adamczewski, Mijung Park |
| 2021 | DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks. Shiyun Xu, Zhiqi Bu |
| 2021 | Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns. Ziping Xu, Amirhossein Meisami, Ambuj Tewari |
| 2021 | Deep Fourier Kernel for Self-Attentive Point Processes. Shixiang Zhu, Minghe Zhang, Ruyi Ding, Yao Xie |
| 2021 | Deep Generative Missingness Pattern-Set Mixture Models. Sahra Ghalebikesabi, Rob Cornish, Chris C. Holmes, Luke J. Kelly |
| 2021 | Deep Neural Networks Are Congestion Games: From Loss Landscape to Wardrop Equilibrium and Beyond. Nina Vesseron, Ievgen Redko, Charlotte Laclau |
| 2021 | Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems. Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He, Wenhao Ding, Henry Lam, Ding Zhao |
| 2021 | Deep Spectral Ranking. Ilkay Yildiz, Jennifer G. Dy, Deniz Erdogmus, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis |
| 2021 | Density of States Estimation for Out of Distribution Detection. Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon |
| 2021 | Designing Transportable Experiments Under S-admissability. My Phan, David Arbour, Drew Dimmery, Anup B. Rao |
| 2021 | Detection and Defense of Topological Adversarial Attacks on Graphs. Yingxue Zhang, Florence Regol, Soumyasundar Pal, Sakif Khan, Liheng Ma, Mark Coates |
| 2021 | Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain. Takahiro Mimori, Keiko Sasada, Hirotaka Matsui, Issei Sato |
| 2021 | Differentiable Causal Discovery Under Unmeasured Confounding. Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser |
| 2021 | Differentiable Divergences Between Time Series. Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert |
| 2021 | Differentiable Greedy Algorithm for Monotone Submodular Maximization: Guarantees, Gradient Estimators, and Applications. Shinsaku Sakaue |
| 2021 | Differentially Private Analysis on Graph Streams. Jalaj Upadhyay, Sarvagya Upadhyay, Raman Arora |
| 2021 | Differentially Private Monotone Submodular Maximization Under Matroid and Knapsack Constraints. Omid Sadeghi, Maryam Fazel |
| 2021 | Differentially Private Online Submodular Maximization. Sebastian Perez-Salazar, Rachel Cummings |
| 2021 | Differentially Private Weighted Sampling. Edith Cohen, Ofir Geri, Tamás Sarlós, Uri Stemmer |
| 2021 | Differentiating the Value Function by using Convex Duality. Sheheryar Mehmood, Peter Ochs |
| 2021 | Direct Loss Minimization for Sparse Gaussian Processes. Yadi Wei, Rishit Sheth, Roni Khardon |
| 2021 | Direct-Search for a Class of Stochastic Min-Max Problems. Sotirios-Konstantinos Anagnostidis, Aurélien Lucchi, Youssef Diouane |
| 2021 | Dirichlet Pruning for Convolutional Neural Networks. Kamil Adamczewski, Mijung Park |
| 2021 | Distribution Regression for Sequential Data. Maud Lemercier, Cristopher Salvi, Theodoros Damoulas, Edwin V. Bonilla, Terry J. Lyons |
| 2021 | Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning. Hitesh Sapkota, Yiming Ying, Feng Chen, Qi Yu |
| 2021 | Does Invariant Risk Minimization Capture Invariance? Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro |
| 2021 | Dominate or Delete: Decentralized Competing Bandits in Serial Dictatorship. Abishek Sankararaman, Soumya Basu, Karthik Abinav Sankararaman |
| 2021 | Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms. Tianyu Ding, Zhihui Zhu, Manolis C. Tsakiris, René Vidal, Daniel P. Robinson |
| 2021 | Dynamic Cutset Networks. Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, Vibhav Gogate |
| 2021 | Efficient Balanced Treatment Assignments for Experimentation. David Arbour, Drew Dimmery, Anup B. Rao |
| 2021 | Efficient Computation and Analysis of Distributional Shapley Values. Yongchan Kwon, Manuel A. Rivas, James Zou |
| 2021 | Efficient Designs Of SLOPE Penalty Sequences In Finite Dimension. Yiliang Zhang, Zhiqi Bu |
| 2021 | Efficient Interpolation of Density Estimators. Paxton Turner, Jingbo Liu, Philippe Rigollet |
| 2021 | Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. Jelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan |
| 2021 | Efficient Statistics for Sparse Graphical Models from Truncated Samples. Arnab Bhattacharyya, Rathin Desai, Sai Ganesh Nagarajan, Ioannis Panageas |
| 2021 | Entropy Partial Transport with Tree Metrics: Theory and Practice. Tam Le, Truyen Nguyen |
| 2021 | Equitable and Optimal Transport with Multiple Agents. Meyer Scetbon, Laurent Meunier, Jamal Atif, Marco Cuturi |
| 2021 | Evading the Curse of Dimensionality in Unconstrained Private GLMs. Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta |
| 2021 | Evaluating Model Robustness and Stability to Dataset Shift. Adarsh Subbaswamy, Roy Adams, Suchi Saria |
| 2021 | Experimental Design for Regret Minimization in Linear Bandits. Andrew Wagenmaker, Julian Katz-Samuels, Kevin Jamieson |
| 2021 | Explicit Regularization of Stochastic Gradient Methods through Duality. Anant Raj, Francis R. Bach |
| 2021 | Exploiting Equality Constraints in Causal Inference. Chi Zhang, Carlos Cinelli, Bryant Chen, Judea Pearl |
| 2021 | Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models. Jan Achterhold, Joerg Stueckler |
| 2021 | Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. Shingo Yashima, Atsushi Nitanda, Taiji Suzuki |
| 2021 | Fair for All: Best-effort Fairness Guarantees for Classification. Anilesh Kollagunta Krishnaswamy, Zhihao Jiang, Kangning Wang, Yu Cheng, Kamesh Munagala |
| 2021 | False Discovery Rates in Biological Networks. Lu Yu, Tobias Kaufmann, Johannes Lederer |
| 2021 | Fast Adaptation with Linearized Neural Networks. Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou |
| 2021 | Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures. Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens |
| 2021 | Fast Statistical Leverage Score Approximation in Kernel Ridge Regression. Yifan Chen, Yun Yang |
| 2021 | Fast and Smooth Interpolation on Wasserstein Space. Sinho Chewi, Julien Clancy, Thibaut Le Gouic, Philippe Rigollet, George Stepaniants, Austin J. Stromme |
| 2021 | Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling. Setareh Ariafar, Zelda Mariet, Dana H. Brooks, Jennifer G. Dy, Jasper Snoek |
| 2021 | Faster Kernel Interpolation for Gaussian Processes. Mohit Yadav, Daniel Sheldon, Cameron Musco |
| 2021 | Federated Learning with Compression: Unified Analysis and Sharp Guarantees. Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, Mehrdad Mahdavi |
| 2021 | Federated Multi-armed Bandits with Personalization. Chengshuai Shi, Cong Shen, Jing Yang |
| 2021 | Federated f-Differential Privacy. Qinqing Zheng, Shuxiao Chen, Qi Long, Weijie J. Su |
| 2021 | Feedback Coding for Active Learning. Gregory Canal, Matthieu R. Bloch, Christopher Rozell |
| 2021 | Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. Han Bao, Masashi Sugiyama |
| 2021 | Finding First-Order Nash Equilibria of Zero-Sum Games with the Regularized Nikaido-Isoda Function. Ioannis C. Tsaknakis, Mingyi Hong |
| 2021 | Finite-Sample Regret Bound for Distributionally Robust Offline Tabular Reinforcement Learning. Zhengqing Zhou, Qinxun Bai, Zhengyuan Zhou, Linhai Qiu, Jose H. Blanchet, Peter W. Glynn |
| 2021 | Fisher Auto-Encoders. Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh |
| 2021 | Flow-based Alignment Approaches for Probability Measures in Different Spaces. Tam Le, Nhat Ho, Makoto Yamada |
| 2021 | Follow Your Star: New Frameworks for Online Stochastic Matching with Known and Unknown Patience. Nathaniel Grammel, Brian Brubach, Will Ma, Aravind Srinivasan |
| 2021 | Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings. Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, David Wipf |
| 2021 | Foundations of Bayesian Learning from Synthetic Data. Harrison Wilde, Jack Jewson, Sebastian J. Vollmer, Chris C. Holmes |
| 2021 | Fourier Bases for Solving Permutation Puzzles. Horace Pan, Risi Kondor |
| 2021 | Fractional moment-preserving initialization schemes for training deep neural networks. Mert Gürbüzbalaban, Yuanhan Hu |
| 2021 | Free-rider Attacks on Model Aggregation in Federated Learning. Yann Fraboni, Richard Vidal, Marco Lorenzi |
| 2021 | Fully Gap-Dependent Bounds for Multinomial Logit Bandit. Jiaqi Yang |
| 2021 | Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions. Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis |
| 2021 | GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences. Mucong Ding, Constantinos Daskalakis, Soheil Feizi |
| 2021 | Gaming Helps! Learning from Strategic Interactions in Natural Dynamics. Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, Juba Ziani |
| 2021 | Generalization Bounds for Stochastic Saddle Point Problems. Junyu Zhang, Mingyi Hong, Mengdi Wang, Shuzhong Zhang |
| 2021 | Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates. Damien Scieur, Lewis Liu, Thomas Pumir, Nicolas Boumal |
| 2021 | Generalized Spectral Clustering via Gromov-Wasserstein Learning. Samir Chowdhury, Tom Needham |
| 2021 | Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties. Lisa Schut, Oscar Key, Rory McGrath, Luca Costabello, Bogdan Sacaleanu, Medb Corcoran, Yarin Gal |
| 2021 | Geometrically Enriched Latent Spaces. Georgios Arvanitidis, Søren Hauberg, Bernhard Schölkopf |
| 2021 | Good Classifiers are Abundant in the Interpolating Regime. Ryan Theisen, Jason M. Klusowski, Michael W. Mahoney |
| 2021 | Goodness-of-Fit Test for Mismatched Self-Exciting Processes. Song Wei, Shixiang Zhu, Minghe Zhang, Yao Xie |
| 2021 | Gradient Descent in RKHS with Importance Labeling. Tomoya Murata, Taiji Suzuki |
| 2021 | Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model. Nafiseh Ghoroghchian, Gautam Dasarathy, Stark C. Draper |
| 2021 | Graph Gamma Process Linear Dynamical Systems. Rahi Kalantari, Mingyuan Zhou |
| 2021 | Graphical Normalizing Flows. Antoine Wehenkel, Gilles Louppe |
| 2021 | Group testing for connected communities. Pavlos Nikolopoulos, Sundara Rajan Srinivasavaradhan, Tao Guo, Christina Fragouli, Suhas N. Diggavi |
| 2021 | Hadamard Wirtinger Flow for Sparse Phase Retrieval. Fan Wu, Patrick Rebeschini |
| 2021 | Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath |
| 2021 | Hidden Cost of Randomized Smoothing. Jeet Mohapatra, Ching-Yun Ko, Lily Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel |
| 2021 | Hierarchical Clustering in General Metric Spaces using Approximate Nearest Neighbors. Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang |
| 2021 | Hierarchical Clustering via Sketches and Hierarchical Correlation Clustering. Danny Vainstein, Vaggos Chatziafratis, Gui Citovsky, Anand Rajagopalan, Mohammad Mahdian, Yossi Azar |
| 2021 | Hierarchical Inducing Point Gaussian Process for Inter-domian Observations. Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham |
| 2021 | High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation. Kristjan H. Greenewald, Karthikeyan Shanmugam, Dmitriy A. Katz |
| 2021 | High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding. Hannah Marienwald, Jean-Baptiste Fermanian, Gilles Blanchard |
| 2021 | Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning. Yunhao Tang, Alp Kucukelbir |
| 2021 | Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes. Nhuong V. Nguyen, Toan N. Nguyen, Phuong Ha Nguyen, Quoc Tran-Dinh, Lam M. Nguyen, Marten van Dijk |
| 2021 | Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent. Frederik Kunstner, Raunak Kumar, Mark Schmidt |
| 2021 | Hyperbolic graph embedding with enhanced semi-implicit variational inference. Ali Lotfi-Rezaabad, Rahi Kalantari, Sriram Vishwanath, Mingyuan Zhou, Jonathan I. Tamir |
| 2021 | Hyperparameter Transfer Learning with Adaptive Complexity. Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau |
| 2021 | Identification of Matrix Joint Block Diagonalization. Yunfeng Cai, Ping Li |
| 2021 | Implicit Regularization via Neural Feature Alignment. Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien |
| 2021 | Improved Complexity Bounds in Wasserstein Barycenter Problem. Darina Dvinskikh, Daniil Tiapkin |
| 2021 | Improved Exploration in Factored Average-Reward MDPs. Mohammad Sadegh Talebi, Anders Jonsson, Odalric Maillard |
| 2021 | Improving Adversarial Robustness via Unlabeled Out-of-Domain Data. Zhun Deng, Linjun Zhang, Amirata Ghorbani, James Zou |
| 2021 | Improving Classifier Confidence using Lossy Label-Invariant Transformations. Sooyong Jang, Insup Lee, James Weimer |
| 2021 | Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression. Ian Covert, Su-In Lee |
| 2021 | Improving predictions of Bayesian neural nets via local linearization. Alexander Immer, Maciej Korzepa, Matthias Bauer |
| 2021 | Independent Innovation Analysis for Nonlinear Vector Autoregressive Process. Hiroshi Morioka, Hermanni Hälvä, Aapo Hyvärinen |
| 2021 | Inductive Mutual Information Estimation: A Convex Maximum-Entropy Copula Approach. Yves-Laurent Kom Samo |
| 2021 | Inference in Stochastic Epidemic Models via Multinomial Approximations. Nick Whiteley, Lorenzo Rimella |
| 2021 | Influence Decompositions For Neural Network Attribution. Kyle Reing, Greg Ver Steeg, Aram Galstyan |
| 2021 | Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits. Marc Abeille, Louis Faury, Clément Calauzènes |
| 2021 | Interpretable Random Forests via Rule Extraction. Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet |
| 2021 | Iterative regularization for convex regularizers. Cesare Molinari, Mathurin Massias, Lorenzo Rosasco, Silvia Villa |
| 2021 | Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation. Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf |
| 2021 | Kernel Interpolation for Scalable Online Gaussian Processes. Samuel Stanton, Wesley J. Maddox, Ian A. Delbridge, Andrew Gordon Wilson |
| 2021 | Kernel regression in high dimensions: Refined analysis beyond double descent. Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens |
| 2021 | LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads. Hossein Shokri Ghadikolaei, Sebastian U. Stich, Martin Jaggi |
| 2021 | Large Scale K-Median Clustering for Stable Clustering Instances. Konstantin Voevodski |
| 2021 | LassoNet: Neural Networks with Feature Sparsity. Ismael Lemhadri, Feng Ruan, Robert Tibshirani |
| 2021 | Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes. Qi Lei, Sai Ganesh Nagarajan, Ioannis Panageas, Xiao Wang |
| 2021 | Latent Derivative Bayesian Last Layer Networks. Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters |
| 2021 | Latent Gaussian process with composite likelihoods and numerical quadrature. Siddharth Ramchandran, Miika Koskinen, Harri Lähdesmäki |
| 2021 | Latent variable modeling with random features. Gregory W. Gundersen, Michael Minyi Zhang, Barbara E. Engelhardt |
| 2021 | Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. Vikas K. Garg, Adam Tauman Kalai, Katrina Ligett, Zhiwei Steven Wu |
| 2021 | Learning Bijective Feature Maps for Linear ICA. Alexander Camuto, Matthew Willetts, Chris C. Holmes, Brooks Paige, Stephen J. Roberts |
| 2021 | Learning Complexity of Simulated Annealing. Avrim Blum, Chen Dan, Saeed Seddighin |
| 2021 | Learning Contact Dynamics using Physically Structured Neural Networks. Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson, Marc Peter Deisenroth |
| 2021 | Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints. Robin Vogel, Aurélien Bellet, Stéphan Clémençon |
| 2021 | Learning GPLVM with arbitrary kernels using the unscented transformation. Daniel Augusto de Souza, Diego Mesquita, João Paulo Pordeus Gomes, César Lincoln C. Mattos |
| 2021 | Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint. Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima |
| 2021 | Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Rahul Jain |
| 2021 | Learning Matching Representations for Individualized Organ Transplantation Allocation. Can Xu, Ahmed M. Alaa, Ioana Bica, Brent D. Ershoff, Maxime Cannesson, Mihaela van der Schaar |
| 2021 | Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir |
| 2021 | Learning Prediction Intervals for Regression: Generalization and Calibration. Haoxian Chen, Ziyi Huang, Henry Lam, Huajie Qian, Haofeng Zhang |
| 2021 | Learning Shared Subgraphs in Ising Model Pairs. Burak Varici, Saurabh Sihag, Ali Tajer |
| 2021 | Learning Smooth and Fair Representations. Xavier Gitiaux, Huzefa Rangwala |
| 2021 | Learning Temporal Point Processes with Intermittent Observations. Vinayak Gupta, Srikanta Bedathur, Sourangshu Bhattacharya, Abir De |
| 2021 | Learning User Preferences in Non-Stationary Environments. Wasim Huleihel, Soumyabrata Pal, Ofer Shayevitz |
| 2021 | Learning the Truth From Only One Side of the Story. Heinrich Jiang, Qijia Jiang, Aldo Pacchiano |
| 2021 | Learning to Defend by Learning to Attack. Haoming Jiang, Zhehui Chen, Yuyang Shi, Bo Dai, Tuo Zhao |
| 2021 | Learning with Gradient Descent and Weakly Convex Losses. Dominic Richards, Mike Rabbat |
| 2021 | Learning with Hyperspherical Uniformity. Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf, Adrian Weller |
| 2021 | Learning with risk-averse feedback under potentially heavy tails. Matthew J. Holland, El Mehdi Haress |
| 2021 | Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model. Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei |
| 2021 | Linear Models are Robust Optimal Under Strategic Behavior. Wei Tang, Chien-Ju Ho, Yang Liu |
| 2021 | Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar |
| 2021 | Linearly Constrained Gaussian Processes with Boundary Conditions. Markus Lange-Hegermann |
| 2021 | List Learning with Attribute Noise. Mahdi Cheraghchi, Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie |
| 2021 | Local Competition and Stochasticity for Adversarial Robustness in Deep Learning. Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos, Sergios Theodoridis |
| 2021 | Local SGD: Unified Theory and New Efficient Methods. Eduard Gorbunov, Filip Hanzely, Peter Richtárik |
| 2021 | Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency. Yuyang Deng, Mehrdad Mahdavi |
| 2021 | Localizing Changes in High-Dimensional Regression Models. Alessandro Rinaldo, Daren Wang, Qin Wen, Rebecca Willett, Yi Yu |
| 2021 | Location Trace Privacy Under Conditional Priors. Casey Meehan, Kamalika Chaudhuri |
| 2021 | Logical Team Q-learning: An approach towards factored policies in cooperative MARL. Lucas Cassano, Ali H. Sayed |
| 2021 | Logistic Q-Learning. Joan Bas-Serrano, Sebastian Curi, Andreas Krause, Gergely Neu |
| 2021 | Longitudinal Variational Autoencoder. Siddharth Ramchandran, Gleb Tikhonov, Kalle Kujanpää, Miika Koskinen, Harri Lähdesmäki |
| 2021 | Low-Rank Generalized Linear Bandit Problems. Yangyi Lu, Amirhossein Meisami, Ambuj Tewari |
| 2021 | Matérn Gaussian Processes on Graphs. Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, Nicolas Durrande |
| 2021 | Maximal Couplings of the Metropolis-Hastings Algorithm. Guanyang Wang, John O'Leary, Pierre Jacob |
| 2021 | Maximizing Agreements for Ranking, Clustering and Hierarchical Clustering via MAX-CUT. Vaggos Chatziafratis, Mohammad Mahdian, Sara Ahmadian |
| 2021 | Mean-Variance Analysis in Bayesian Optimization under Uncertainty. Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi |
| 2021 | Measure Transport with Kernel Stein Discrepancy. Matthew Fisher, Tui Nolan, Matthew M. Graham, Dennis Prangle, Chris J. Oates |
| 2021 | Meta Learning in the Continuous Time Limit. Ruitu Xu, Lin Chen, Amin Karbasi |
| 2021 | Meta-Learning Divergences for Variational Inference. Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang |
| 2021 | Minimal enumeration of all possible total effects in a Markov equivalence class. F. Richard Guo, Emilija Perkovic |
| 2021 | Minimax Estimation of Laplacian Constrained Precision Matrices. Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar |
| 2021 | Minimax Model Learning. Cameron Voloshin, Nan Jiang, Yisong Yue |
| 2021 | Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs. Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani |
| 2021 | Mirror Descent View for Neural Network Quantization. Thalaiyasingam Ajanthan, Kartik Gupta, Philip H. S. Torr, Richard Hartley, Puneet K. Dokania |
| 2021 | Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent. Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro |
| 2021 | Misspecification in Prediction Problems and Robustness via Improper Learning. Annie Marsden, John C. Duchi, Gregory Valiant |
| 2021 | Model updating after interventions paradoxically introduces bias. James Liley, Samuel R. Emerson, Bilal A. Mateen, Catalina A. Vallejos, Louis J. M. Aslett, Sebastian J. Vollmer |
| 2021 | Moment-Based Variational Inference for Stochastic Differential Equations. Christian Wildner, Heinz Koeppl |
| 2021 | Momentum Improves Optimization on Riemannian Manifolds. Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi |
| 2021 | Multi-Armed Bandits with Cost Subsidy. Deeksha Sinha, Karthik Abinav Sankararaman, Abbas Kazerouni, Vashist Avadhanula |
| 2021 | Multi-Fidelity High-Order Gaussian Processes for Physical Simulation. Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe |
| 2021 | Multitask Bandit Learning Through Heterogeneous Feedback Aggregation. Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri |
| 2021 | Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning. Ming Yin, Yu Bai, Yu-Xiang Wang |
| 2021 | Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications. Guillaume Ausset, Stéphan Clémençon, François Portier |
| 2021 | Nested Barycentric Coordinate System as an Explicit Feature Map. Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch, Ofir Pele |
| 2021 | Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference. Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe |
| 2021 | Neural Enhanced Belief Propagation on Factor Graphs. Victor Garcia Satorras, Max Welling |
| 2021 | Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers. Alex Lamb, Anirudh Goyal, Agnieszka Slowik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio |
| 2021 | No-Regret Algorithms for Private Gaussian Process Bandit Optimization. Abhimanyu Dubey |
| 2021 | No-Regret Reinforcement Learning with Heavy-Tailed Rewards. Vincent Zhuang, Yanan Sui |
| 2021 | No-regret Algorithms for Multi-task Bayesian Optimization. Sayak Ray Chowdhury, Aditya Gopalan |
| 2021 | Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic |
| 2021 | Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization. Tianyi Liu, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao |
| 2021 | Non-Stationary Off-Policy Optimization. Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed |
| 2021 | Non-Volume Preserving Hamiltonian Monte Carlo and No-U-TurnSamplers. Hadi Mohasel Afshar, Rafael Oliveira, Sally Cripps |
| 2021 | Non-asymptotic Performance Guarantees for Neural Estimation of f-Divergences. Sreejith Sreekumar, Zhengxin Zhang, Ziv Goldfeld |
| 2021 | Nonlinear Functional Output Regression: A Dictionary Approach. Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc |
| 2021 | Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks. Huichen Li, Linyi Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, Bo Li |
| 2021 | Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. Alicia Curth, Mihaela van der Schaar |
| 2021 | Nonparametric Variable Screening with Optimal Decision Stumps. Jason M. Klusowski, Peter M. Tian |
| 2021 | Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation. Yuki Ohnishi, Jean Honorio |
| 2021 | Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi |
| 2021 | Offline detection of change-points in the mean for stationary graph signals. Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos |
| 2021 | On Data Efficiency of Meta-learning. Maruan Al-Shedivat, Liam Li, Eric P. Xing, Ameet Talwalkar |
| 2021 | On Information Gain and Regret Bounds in Gaussian Process Bandits. Sattar Vakili, Kia Khezeli, Victor Picheny |
| 2021 | On Learning Continuous Pairwise Markov Random Fields. Abhin Shah, Devavrat Shah, Gregory W. Wornell |
| 2021 | On Multilevel Monte Carlo Unbiased Gradient Estimation for Deep Latent Variable Models. Yuyang Shi, Rob Cornish |
| 2021 | On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan |
| 2021 | On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. Alain Durmus, Pablo Jiménez, Eric Moulines, Salem Said |
| 2021 | On the Absence of Spurious Local Minima in Nonlinear Low-Rank Matrix Recovery Problems. Yingjie Bi, Javad Lavaei |
| 2021 | On the Consistency of Metric and Non-Metric K-Medoids. He Jiang, Ery Arias-Castro |
| 2021 | On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow. Youssef Mroueh, Truyen Nguyen |
| 2021 | On the Effect of Auxiliary Tasks on Representation Dynamics. Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney |
| 2021 | On the Faster Alternating Least-Squares for CCA. Zhiqiang Xu, Ping Li |
| 2021 | On the Generalization Properties of Adversarial Training. Yue Xing, Qifan Song, Guang Cheng |
| 2021 | On the High Accuracy Limitation of Adaptive Property Estimation. Yanjun Han |
| 2021 | On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning. Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan O. Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra |
| 2021 | On the Linear Convergence of Policy Gradient Methods for Finite MDPs. Jalaj Bhandari, Daniel Russo |
| 2021 | On the Memory Mechanism of Tensor-Power Recurrent Models. Hejia Qiu, Chao Li, Ying Weng, Zhun Sun, Xingyu He, Qibin Zhao |
| 2021 | On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression. Jeongyeol Kwon, Nhat Ho, Constantine Caramanis |
| 2021 | On the Privacy Properties of GAN-generated Samples. Zinan Lin, Vyas Sekar, Giulia Fanti |
| 2021 | On the Suboptimality of Negative Momentum for Minimax Optimization. Guodong Zhang, Yuanhao Wang |
| 2021 | On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions. Kai Brügge, Asja Fischer, Christian Igel |
| 2021 | On the number of linear functions composing deep neural network: Towards a refined definition of neural networks complexity. Yuuki Takai, Akiyoshi Sannai, Matthieu Cordonnier |
| 2021 | On the proliferation of support vectors in high dimensions. Daniel Hsu, Vidya Muthukumar, Ji Xu |
| 2021 | On the role of data in PAC-Bayes. Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, Daniel M. Roy |
| 2021 | One-Round Communication Efficient Distributed M-Estimation. Yajie Bao, Weijia Xiong |
| 2021 | One-Sketch-for-All: Non-linear Random Features from Compressed Linear Measurements. Xiaoyun Li, Ping Li |
| 2021 | One-pass Stochastic Gradient Descent in overparametrized two-layer neural networks. Hanjing Zhu, Jiaming Xu |
| 2021 | Online Active Model Selection for Pre-trained Classifiers. Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlas, Johannes Rausch, Ce Zhang, Andreas Krause |
| 2021 | Online Forgetting Process for Linear Regression Models. Yuantong Li, Chi-hua Wang, Guang Cheng |
| 2021 | Online Model Selection for Reinforcement Learning with Function Approximation. Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill |
| 2021 | Online Robust Control of Nonlinear Systems with Large Uncertainty. Dimitar Ho, Hoang Minh Le, John Doyle, Yisong Yue |
| 2021 | Online Sparse Reinforcement Learning. Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang |
| 2021 | Online k-means Clustering. Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom |
| 2021 | Online probabilistic label trees. Marek Wydmuch, Kalina Jasinska-Kobus, Devanathan Thiruvenkatachari, Krzysztof Dembczynski |
| 2021 | Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. Onur Teymur, Jackson Gorham, Marina Riabiz, Chris J. Oates |
| 2021 | Optimal query complexity for private sequential learning against eavesdropping. Jiaming Xu, Kuang Xu, Dana Yang |
| 2021 | Optimizing Percentile Criterion using Robust MDPs. Bahram Behzadian, Reazul Hasan Russel, Marek Petrik, Chin Pang Ho |
| 2021 | PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash Mansinghka |
| 2021 | Parametric Programming Approach for More Powerful and General Lasso Selective Inference. Vo Nguyen Le Duy, Ichiro Takeuchi |
| 2021 | Power of Hints for Online Learning with Movement Costs. Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit |
| 2021 | Prediction with Finitely many Errors Almost Surely. Changlong Wu, Narayana Santhanam |
| 2021 | Predictive Complexity Priors. Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato |
| 2021 | Predictive Power of Nearest Neighbors Algorithm under Random Perturbation. Yue Xing, Qifan Song, Guang Cheng |
| 2021 | Principal Component Regression with Semirandom Observations via Matrix Completion. Aditya Bhaskara, Aravinda Kanchana Ruwanpathirana, Maheshakya Wijewardena |
| 2021 | Principal Subspace Estimation Under Information Diffusion. Fan Zhou, Ping Li, Zhixin Zhou |
| 2021 | Private optimization without constraint violations. Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii, Ellen Vitercik |
| 2021 | Probabilistic Sequential Matrix Factorization. Ömer Deniz Akyildiz, Gerrit J. J. van den Burg, Theodoros Damoulas, Mark F. J. Steel |
| 2021 | Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits. Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran |
| 2021 | Product Manifold Learning. Sharon Zhang, Amit Moscovich, Amit Singer |
| 2021 | Projection-Free Optimization on Uniformly Convex Sets. Thomas Kerdreux, Alexandre d'Aspremont, Sebastian Pokutta |
| 2021 | Provable Hierarchical Imitation Learning via EM. Zhiyu Zhang, Ioannis Ch. Paschalidis |
| 2021 | Provably Efficient Actor-Critic for Risk-Sensitive and Robust Adversarial RL: A Linear-Quadratic Case. Yufeng Zhang, Zhuoran Yang, Zhaoran Wang |
| 2021 | Provably Efficient Safe Exploration via Primal-Dual Policy Optimization. Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, Mihailo R. Jovanovic |
| 2021 | Provably Safe PAC-MDP Exploration Using Analogies. Melrose Roderick, Vaishnavh Nagarajan, J. Zico Kolter |
| 2021 | Q-learning with Logarithmic Regret. Kunhe Yang, Lin F. Yang, Simon S. Du |
| 2021 | Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models. Sasi Kumar Murakonda, Reza Shokri, George Theodorakopoulos |
| 2021 | Quantum Tensor Networks, Stochastic Processes, and Weighted Automata. Sandesh Adhikary, Siddarth Srinivasan, Jacob Miller, Guillaume Rabusseau, Byron Boots |
| 2021 | Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time. Alan Kuhnle |
| 2021 | Random Coordinate Underdamped Langevin Monte Carlo. Zhiyan Ding, Qin Li, Jianfeng Lu, Stephen J. Wright |
| 2021 | RankDistil: Knowledge Distillation for Ranking. Sashank J. Reddi, Rama Kumar Pasumarthi, Aditya Krishna Menon, Ankit Singh Rawat, Felix X. Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar |
| 2021 | Rao-Blackwellised parallel MCMC. Tobias Schwedes, Ben Calderhead |
| 2021 | Rate-Regularization and Generalization in Variational Autoencoders. Alican Bozkurt, Babak Esmaeili, Jean-Baptiste Tristan, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent |
| 2021 | Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization. Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu |
| 2021 | Reaping the Benefits of Bundling under High Production Costs. Will Ma, David Simchi-Levi |
| 2021 | Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. Leena C. Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar |
| 2021 | Regression Discontinuity Design under Self-selection. Sida Peng, Yang Ning |
| 2021 | Regret Minimization for Causal Inference on Large Treatment Space. Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima |
| 2021 | Regret-Optimal Filtering. Oron Sabag, Babak Hassibi |
| 2021 | Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network. Tianyang Hu, Wenjia Wang, Cong Lin, Guang Cheng |
| 2021 | Regularized ERM on random subspaces. Andrea Della Vecchia, Jaouad Mourtada, Ernesto De Vito, Lorenzo Rosasco |
| 2021 | Regularized Policies are Reward Robust. Hisham Husain, Kamil Ciosek, Ryota Tomioka |
| 2021 | Reinforcement Learning for Constrained Markov Decision Processes. Ather Gattami, Qinbo Bai, Vaneet Aggarwal |
| 2021 | Reinforcement Learning for Mean Field Games with Strategic Complementarities. Ki-Yeob Lee, Desik Rengarajan, Dileep M. Kalathil, Srinivas Shakkottai |
| 2021 | Reinforcement Learning in Parametric MDPs with Exponential Families. Sayak Ray Chowdhury, Aditya Gopalan, Odalric-Ambrym Maillard |
| 2021 | Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang |
| 2021 | Revisiting Projection-free Online Learning: the Strongly Convex Case. Ben Kretzu, Dan Garber |
| 2021 | Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization. Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand, Thomas Hofmann, Roy S. Smith |
| 2021 | Ridge Regression with Over-parametrized Two-Layer Networks Converge to Ridgelet Spectrum. Sho Sonoda, Isao Ishikawa, Masahiro Ikeda |
| 2021 | Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration. Shengjia Zhao, Stefano Ermon |
| 2021 | Robust Imitation Learning from Noisy Demonstrations. Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama |
| 2021 | Robust Learning under Strong Noise via SQs. Ioannis Anagnostides, Themis Gouleakis, Ali Marashian |
| 2021 | Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers. Lunjia Hu, Omer Reingold |
| 2021 | Robust and Private Learning of Halfspaces. Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen |
| 2021 | Robust hypothesis testing and distribution estimation in Hellinger distance. Ananda Theertha Suresh |
| 2021 | Robustness and scalability under heavy tails, without strong convexity. Matthew Holland |
| 2021 | SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups. Hyun-Suk Lee, Cong Shen, William R. Zame, Jang-Won Lee, Mihaela van der Schaar |
| 2021 | SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. Robert M. Gower, Othmane Sebbouh, Nicolas Loizou |
| 2021 | SONIA: A Symmetric Blockwise Truncated Optimization Algorithm. Majid Jahani, MohammadReza Nazari, Rachael Tappenden, Albert S. Berahas, Martin Takác |
| 2021 | Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms. Tengyu Xu, Yingbin Liang |
| 2021 | Sample Elicitation. Jiaheng Wei, Zuyue Fu, Yang Liu, Xingyu Li, Zhuoran Yang, Zhaoran Wang |
| 2021 | Sample efficient learning of image-based diagnostic classifiers via probabilistic labels. Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth Hareendranathan, Jeevesh Kapur, Jacob L. Jaremko, Russell Greiner |
| 2021 | Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC. Priyank Jaini, Didrik Nielsen, Max Welling |
| 2021 | Scalable Constrained Bayesian Optimization. David Eriksson, Matthias Poloczek |
| 2021 | Scalable Gaussian Process Variational Autoencoders. Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch |
| 2021 | Selective Classification via One-Sided Prediction. Aditya Gangrade, Anil Kag, Venkatesh Saligrama |
| 2021 | Self-Concordant Analysis of Generalized Linear Bandits with Forgetting. Yoan Russac, Louis Faury, Olivier Cappé, Aurélien Garivier |
| 2021 | Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry. Qadeer Khan, Patrick Wenzel, Daniel Cremers |
| 2021 | Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees. Alessio Mazzetto, Dylan Sam, Andrew Park, Eli Upfal, Stephen H. Bach |
| 2021 | Semi-Supervised Learning with Meta-Gradient. Taihong Xiao, Xin-Yu Zhang, Hao-Lin Jia, Ming-Ming Cheng, Ming-Hsuan Yang |
| 2021 | Sequential Random Sampling Revisited: Hidden Shuffle Method. Michael Shekelyan, Graham Cormode |
| 2021 | Shadow Manifold Hamiltonian Monte Carlo. Christopher van der Heide, Fred Roosta, Liam Hodgkinson, Dirk P. Kroese |
| 2021 | Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg |
| 2021 | Sharp Analysis of a Simple Model for Random Forests. Jason M. Klusowski |
| 2021 | Shuffled Model of Differential Privacy in Federated Learning. Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh |
| 2021 | Significance of Gradient Information in Bayesian Optimization. Shubhanshu Shekhar, Tara Javidi |
| 2021 | Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series. Xing Han, Sambarta Dasgupta, Joydeep Ghosh |
| 2021 | Sketch based Memory for Neural Networks. Rina Panigrahy, Xin Wang, Manzil Zaheer |
| 2021 | Smooth Bandit Optimization: Generalization to Holder Space. Yusha Liu, Yining Wang, Aarti Singh |
| 2021 | Sparse Algorithms for Markovian Gaussian Processes. William J. Wilkinson, Arno Solin, Vincent Adam |
| 2021 | Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone |
| 2021 | Spectral Tensor Train Parameterization of Deep Learning Layers. Anton Obukhov, Maxim V. Rakhuba, Alexander Liniger, Zhiwu Huang, Stamatios Georgoulis, Dengxin Dai, Luc Van Gool |
| 2021 | Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss. Zhenhuan Yang, Yunwen Lei, Siwei Lyu, Yiming Ying |
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| 2021 | Stable ResNet. Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau |
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