COLT 2022

162 papers

(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping Prateek Varshney, Abhradeep Thakurta, Prateek Jain
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A Bounded-Noise Mechanism for Differential Privacy Yuval Dagan, Gil Kur
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A Private and Computationally-Efficient Estimator for Unbounded Gaussians Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan Ullman
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A Sharp Memory-Regret Trade-Off for Multi-Pass Streaming Bandits Arpit Agarwal, Sanjeev Khanna, Prathamesh Patil
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Accelerated SGD for Non-Strongly-Convex Least Squares Aditya Varre, Nicolas Flammarion
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Adaptive Bandit Convex Optimization with Heterogeneous Curvature Haipeng Luo, Mengxiao Zhang, Peng Zhao
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Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds Shinji Ito, Taira Tsuchiya, Junya Honda
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An Efficient Minimax Optimal Estimator for Multivariate Convex Regression Gil Kur, Eli Putterman
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Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev Sinho Chewi, Murat A Erdogdu, Mufan Li, Ruoqi Shen, Shunshi Zhang
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Approximate Cluster Recovery from Noisy Labels Buddhima Gamlath, Silvio Lattanzi, Ashkan Norouzi-Fard, Ola Svensson
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Assemblies of Neurons Learn to Classify Well-Separated Distributions Max Dabagia, Santosh S Vempala, Christos Papadimitriou
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Benign Overfitting Without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data Spencer Frei, Niladri S Chatterji, Peter Bartlett
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Better Private Algorithms for Correlation Clustering Daogao Liu
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Beyond No Regret: Instance-Dependent PAC Reinforcement Learning Andrew J Wagenmaker, Max Simchowitz, Kevin Jamieson
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Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales Jonathan Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan
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Can Q-Learning Be Improved with Advice? Noah Golowich, Ankur Moitra
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Chained Generalisation Bounds Eugenio Clerico, Amitis Shidani, George Deligiannidis, Arnaud Doucet
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Chasing Convex Bodies and Functions with Black-Box Advice Nicolas Christianson, Tinashe Handina, Adam Wierman
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Clustering with Queries Under Semi-Random Noise Alberto Del Pia, Mingchen Ma, Christos Tzamos
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Community Recovery in the Degree-Heterogeneous Stochastic Block Model Vincent Cohen-Addad, Frederik Mallmann-Trenn, David Saulpic
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Complete Policy Regret Bounds for Tallying Bandits Dhruv Malik, Yuanzhi Li, Aarti Singh
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Computational-Statistical Gap in Reinforcement Learning Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan
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Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits Haipeng Luo, Mengxiao Zhang, Peng Zhao, Zhi-Hua Zhou
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Corruption-Robust Contextual Search Through Density Updates Renato Paes Leme, Chara Podimata, Jon Schneider
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Damped Online Newton Step for Portfolio Selection Zakaria Mhammedi, Alexander Rakhlin
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Depth and Feature Learning Are Provably Beneficial for Neural Network Discriminators Carles Domingo-Enrich
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Derivatives and Residual Distribution of Regularized M-Estimators with Application to Adaptive Tuning Pierre C Bellec, Yiwei Shen
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Differential Privacy and Robust Statistics in High Dimensions Xiyang Liu, Weihao Kong, Sewoong Oh
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Dimension-Free Convergence Rates for Gradient Langevin Dynamics in RKHS Boris Muzellec, Kanji Sato, Mathurin Massias, Taiji Suzuki
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Efficient Convex Optimization Requires Superlinear Memory Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant
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Efficient Decentralized Multi-Agent Learning in Asymmetric Queuing Systems Daniel Freund, Thodoris Lykouris, Wentao Weng
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Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics Asaf B Cassel, Alon Cohen, Tomer Koren
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Efficient Projection-Free Online Convex Optimization with Membership Oracle Zakaria Mhammedi
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Eigenspace Restructuring: A Principle of Space and Frequency in Neural Networks Lechao Xiao
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EM’s Convergence in Gaussian Latent Tree Models Yuval Dagan, Vardis Kandiros, Constantinos Daskalakis
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Exact Community Recovery in Correlated Stochastic Block Models Julia Gaudio, Miklos Z. Racz, Anirudh Sridhar
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Fast Algorithm for Overcomplete Order-3 Tensor Decomposition Jingqiu Ding, Tommaso d’Orsi, Chih-Hung Liu, David Steurer, Stefan Tiegel
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Faster Online Calibration Without Randomization: Interval Forecasts and the Power of Two Choices Chirag Gupta, Aaditya Ramdas
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From Sampling to Optimization on Discrete Domains with Applications to Determinant Maximization Nima Anari, Thuy-Duong Vuong
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Gardner Formula for Ising Perceptron Models at Small Densities Erwin Bolthausen, Shuta Nakajima, Nike Sun, Changji Xu
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Generalization Bounds for Data-Driven Numerical Linear Algebra Peter Bartlett, Piotr Indyk, Tal Wagner
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Generalization Bounds via Convex Analysis Gabor Lugosi, Gergely Neu
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Hardness of Maximum Likelihood Learning of DPPs Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie
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Hierarchical Clustering in Graph Streams: Single-Pass Algorithms and Space Lower Bounds Sepehr Assadi, Vaggos Chatziafratis, Jakub Łącki, Vahab Mirrokni, Chen Wang
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High-Dimensional Projection Pursuit: Outer Bounds and Applications to Interpolation in Neural Networks Kangjie Zhou, Andrea Montanari
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Horizon-Free Reinforcement Learning in Polynomial Time: The Power of Stationary Policies Zihan Zhang, Xiangyang Ji, Simon Du
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How Catastrophic Can Catastrophic Forgetting Be in Linear Regression? Itay Evron, Edward Moroshko, Rachel Ward, Nathan Srebro, Daniel Soudry
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Improved Analysis for a Proximal Algorithm for Sampling Yongxin Chen, Sinho Chewi, Adil Salim, Andre Wibisono
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Improved Parallel Algorithm for Minimum Cost Submodular Cover Problem Yingli Ran, Zhao Zhang, Shaojie Tang
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Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm Meena Jagadeesan, Ilya Razenshteyn, Suriya Gunasekar
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Kernel Interpolation in Sobolev Spaces Is Not Consistent in Low Dimensions Simon Buchholz
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Label Noise (stochastic) Gradient Descent Implicitly Solves the Lasso for Quadratic Parametrisation Loucas Pillaud Vivien, Julien Reygner, Nicolas Flammarion
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Lattice-Based Methods Surpass Sum-of-Squares in Clustering Ilias Zadik, Min Jae Song, Alexander S Wein, Joan Bruna
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Learning a Single Neuron with Adversarial Label Noise via Gradient Descent Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis
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Learning GMMs with Nearly Optimal Robustness Guarantees Allen Liu, Ankur Moitra
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Learning Low Degree Hypergraphs Eric Balkanski, Oussama Hanguir, Shatian Wang
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Learning to Control Linear Systems Can Be Hard Anastasios Tsiamis, Ingvar M Ziemann, Manfred Morari, Nikolai Matni, George J. Pappas
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Learning with Metric Losses Dan Tsir Cohen, Aryeh Kontorovich
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Low-Degree Multicalibration Parikshit Gopalan, Michael P Kim, Mihir A Singhal, Shengjia Zhao
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Making SGD Parameter-Free Yair Carmon, Oliver Hinder
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Mean-Field Nonparametric Estimation of Interacting Particle Systems Rentian Yao, Xiaohui Chen, Yun Yang
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Memorize to Generalize: On the Necessity of Interpolation in High Dimensional Linear Regression Chen Cheng, John Duchi, Rohith Kuditipudi
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Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini’s Regret Tor Lattimore
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Minimax Regret on Patterns Using Kullback-Leibler Divergence Covering Jennifer Tang
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Minimax Regret Optimization for Robust Machine Learning Under Distribution Shift Alekh Agarwal, Tong Zhang
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Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization Under Infinite Noise Variance Nuri Mert Vural, Lu Yu, Krishna Balasubramanian, Stanislav Volgushev, Murat A Erdogdu
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Monotone Learning Olivier J Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer
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Multi-Agent Learning for Iterative Dominance Elimination: Formal Barriers and New Algorithms Jibang Wu, Haifeng Xu, Fan Yao
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Multilevel Optimization for Inverse Problems Simon Weissmann, Ashia Wilson, Jakob Zech
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Near Optimal Efficient Decoding from Pooled Data Max Hahn-Klimroth, Noela Müller
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Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise Ilias Diakonikolas, Daniel Kane
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Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals Daniel J Hsu, Clayton H Sanford, Rocco Servedio, Emmanouil Vasileios Vlatakis-Gkaragkounis
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Negative Curvature Obstructs Acceleration for Strongly Geodesically Convex Optimization, Even with Exact First-Order Oracles Christopher Criscitiello, Nicolas Boumal
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Neural Networks Can Learn Representations with Gradient Descent Alexandru Damian, Jason Lee, Mahdi Soltanolkotabi
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New Projection-Free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees Dan Garber, Ben Kretzu
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Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares Blake Woodworth, Francis Bach, Alessandro Rudi
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Non-Gaussian Component Analysis via Lattice Basis Reduction Ilias Diakonikolas, Daniel Kane
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Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-Efficiency of Posterior Sampling Alekh Agarwal, Tong Zhang
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Offline Reinforcement Learning with Realizability and Single-Policy Concentrability Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason Lee
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Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation Dylan J Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu
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On Almost Sure Convergence Rates of Stochastic Gradient Methods Jun Liu, Ye Yuan
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On Characterizations of Learnability with Computable Learners Tom F. Sterkenburg
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On the Benefits of Large Learning Rates for Kernel Methods Gaspard Beugnot, Julien Mairal, Alessandro Rudi
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On the Memory Complexity of Uniformity Testing Tomer Berg, Or Ordentlich, Ofer Shayevitz
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On the Power of Adaptivity in Statistical Adversaries Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan
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On the Role of Channel Capacity in Learning Gaussian Mixture Models Elad Romanov, Tamir Bendory, Or Ordentlich
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On the Well-Spread Property and Its Relation to Linear Regression Hongjie Chen, Tommaso d’Orsi
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Online Learning to Transport via the Minimal Selection Principle Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Chris Ryan
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Open Problem: Better Differentially Private Learning Algorithms with Margin Guarantees Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
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Open Problem: Do You Pay for Privacy in Online Learning? Amartya Sanyal, Giorgia Ramponi
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Open Problem: Finite-Time Instance Dependent Optimality for Stochastic Online Learning with Feedback Graphs Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert
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Open Problem: Optimal Best Arm Identification with Fixed-Budget Chao Qin
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Open Problem: Properly Learning Decision Trees in Polynomial Time? Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan
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Open Problem: Regret Bounds for Noise-Free Kernel-Based Bandits Sattar Vakili
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Open Problem: Running Time Complexity of Accelerated $\ell_1$-Regularized PageRank Kimon Fountoulakis, Shenghao Yang
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Optimal and Instance-Dependent Guarantees for Markovian Linear Stochastic Approximation Wenlong Mou, Ashwin Pananjady, Martin Wainwright, Peter Bartlett
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Optimal Mean Estimation Without a Variance Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter Bartlett, Michael Jordan
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Optimal SQ Lower Bounds for Learning Halfspaces with Massart Noise Rajai Nasser, Stefan Tiegel
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Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun
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Optimization-Based Separations for Neural Networks Itay Safran, Jason Lee
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Orthogonal Statistical Learning with Self-Concordant Loss Lang Liu, Carlos Cinelli, Zaid Harchaoui
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Parameter-Free Mirror Descent Andrew Jacobsen, Ashok Cutkosky
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Policy Optimization for Stochastic Shortest Path Liyu Chen, Haipeng Luo, Aviv Rosenberg
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Private and Polynomial Time Algorithms for Learning Gaussians and Beyond Hassan Ashtiani, Christopher Liaw
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Private Convex Optimization via Exponential Mechanism Sivakanth Gopi, Yin Tat Lee, Daogao Liu
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Private High-Dimensional Hypothesis Testing Shyam Narayanan
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Private Matrix Approximation and Geometry of Unitary Orbits Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Thakurta, Nisheeth K. Vishnoi
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Private Robust Estimation by Stabilizing Convex Relaxations Pravesh Kothari, Pasin Manurangsi, Ameya Velingker
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Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States Julian Zimmert, Naman Agarwal, Satyen Kale
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Random Graph Matching in Geometric Models: The Case of Complete Graphs Haoyu Wang, Yihong Wu, Jiaming Xu, Israel Yolou
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Rate of Convergence of Polynomial Networks to Gaussian Processes Adam Klukowski
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Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms Milad Sefidgaran, Amin Gohari, Gaël Richard, Umut Simsekli
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Realizable Learning Is All You Need Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan
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Return of the Bias: Almost Minimax Optimal High Probability Bounds for Adversarial Linear Bandits Julian Zimmert, Tor Lattimore
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Risk Bounds for Aggregated Shallow Neural Networks Using Gaussian Priors Laura Tinsi, Arnak Dalalyan
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Robust Estimation for Random Graphs Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang
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Robust Sparse Mean Estimation via Sum of Squares Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas
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Robustly-Reliable Learners Under Poisoning Attacks Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma
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ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm Chris Junchi Li, Wenlong Mou, Martin Wainwright, Michael Jordan
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Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information Yonathan Efroni, Dylan J Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
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Sampling Approximately Low-Rank Ising Models: MCMC Meets Variational Methods Frederic Koehler, Holden Lee, Andrej Risteski
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Scale-Free Unconstrained Online Learning for Curved Losses Jack J. Mayo, Hedi Hadiji, Tim Erven
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Self-Consistency of the Fokker Planck Equation Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani
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Sharp Constants in Uniformity Testing via the Huber Statistic Shivam Gupta, Eric Price
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Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Yujia Jin, Aaron Sidford, Kevin Tian
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Single Trajectory Nonparametric Learning of Nonlinear Dynamics Ingvar M Ziemann, Henrik Sandberg, Nikolai Matni
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Smoothed Online Learning Is as Easy as Statistical Learning Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin
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Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond Matan Schliserman, Tomer Koren
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Statistical and Computational Phase Transitions in Group Testing Amin Coja-Oghlan, Oliver Gebhard, Max Hahn-Klimroth, Alexander S Wein, Ilias Zadik
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Statistical Estimation and Online Inference via Local SGD Xiang Li, Jiadong Liang, Xiangyu Chang, Zhihua Zhang
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Stochastic Linear Optimization Never Overfits with Quadratically-Bounded Losses on General Data Matus Telgarsky
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Stochastic Variance Reduction for Variational Inequality Methods Ahmet Alacaoglu, Yura Malitsky
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Strategizing Against Learners in Bayesian Games Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan
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Streaming Algorithms for Ellipsoidal Approximation of Convex Polytopes Yury Makarychev, Naren Sarayu Manoj, Max Ovsiankin
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Strong Gaussian Approximation for the Sum of Random Vectors Nazar Buzun, Nikolay Shvetsov, Dmitry V. Dylov
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Strong Memory Lower Bounds for Learning Natural Models Gavin Brown, Mark Bun, Adam Smith
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The Dynamics of Riemannian Robbins-Monro Algorithms Mohammad Reza Karimi, Ya-Ping Hsieh, Panayotis Mertikopoulos, Andreas Krause
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The Implicit Bias of Benign Overfitting Ohad Shamir
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The Merged-Staircase Property: A Necessary and Nearly Sufficient Condition for SGD Learning of Sparse Functions on Two-Layer Neural Networks Emmanuel Abbe, Enric Boix Adsera, Theodor Misiakiewicz
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The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with No Communication Allen X Liu, Mark Sellke
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The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance Matthew Faw, Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari, Sanjay Shakkottai, Rachel Ward
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The Price of Tolerance in Distribution Testing Clement L Canonne, Ayush Jain, Gautam Kamath, Jerry Li
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The Query Complexity of Local Search and Brouwer in Rounds Simina Branzei, Jiawei Li
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The Query Complexity of Sampling from Strongly Log-Concave Distributions in One Dimension Sinho Chewi, Patrik R Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet
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The Role of Interactivity in Structured Estimation Jayadev Acharya, Clement L. Canonne, Ziteng Sun, Himanshu Tyagi
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The Structured Abstain Problem and the Lovász Hinge Jessica J Finocchiaro, Rafael Frongillo, Enrique B Nueve
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Thompson Sampling Achieves $\tilde{O}(\sqrt{T})$ Regret in Linear Quadratic Control Taylan Kargin, Sahin Lale, Kamyar Azizzadenesheli, Animashree Anandkumar, Babak Hassibi
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Tight Query Complexity Bounds for Learning Graph Partitions Xizhi Liu, Sayan Mukherjee
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Toward Instance-Optimal State Certification with Incoherent Measurements Sitan Chen, Jerry Li, Ryan O’Donnell
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Towards a Theory of Non-Log-Concave Sampling:First-Order Stationarity Guarantees for Langevin Monte Carlo Krishna Balasubramanian, Sinho Chewi, Murat A Erdogdu, Adil Salim, Shunshi Zhang
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Towards Optimal Algorithms for Multi-Player Bandits Without Collision Sensing Information Wei Huang, Richard Combes, Cindy Trinh
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Trace Norm Regularization for Multi-Task Learning with Scarce Data Etienne Boursier, Mikhail Konobeev, Nicolas Flammarion
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Tracking Most Significant Arm Switches in Bandits Joe Suk, Samory Kpotufe
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Two-Sided Weak Submodularity for Matroid Constrained Optimization and Regression Theophile Thiery, Justin Ward
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Understanding Riemannian Acceleration via a Proximal Extragradient Framework Jikai Jin, Suvrit Sra
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Uniform Stability for First-Order Empirical Risk Minimization Amit Attia, Tomer Koren
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Universal Online Learning with Bounded Loss: Reduction to Binary Classification Moise Blanchard, Romain Cosson
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Universal Online Learning: An Optimistically Universal Learning Rule Moise Blanchard
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Universality of Empirical Risk Minimization Andrea Montanari, Basil N. Saeed
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Wasserstein GANs with Gradient Penalty Compute Congested Transport Tristan Milne, Adrian I Nachman
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When Is Partially Observable Reinforcement Learning Not Scary? Qinghua Liu, Alan Chung, Csaba Szepesvari, Chi Jin
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Width Is Less Important than Depth in ReLU Neural Networks Gal Vardi, Gilad Yehudai, Ohad Shamir
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