Conference on Learning Theory 2020: Preface

Jacob Abernethy, Shivani Agarwal ; PMLR 125:1-2

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Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit

Jayadev Acharya, Clément L Canonne, Yanjun Han, Ziteng Sun, Himanshu Tyagi ; PMLR 125:3-40

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Distributed Signal Detection under Communication Constraints

Jayadev Acharya, Clément L Canonne, Himanshu Tyagi ; PMLR 125:41-63

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Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes

Alekh Agarwal, Sham M Kakade, Jason D Lee, Gaurav Mahajan ; PMLR 125:64-66

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Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal

Alekh Agarwal, Sham Kakade, Lin F. Yang ; PMLR 125:67-83

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From Nesterov’s Estimate Sequence to Riemannian Acceleration

Kwangjun Ahn, Suvrit Sra ; PMLR 125:84-118

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Closure Properties for Private Classification and Online Prediction

Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer ; PMLR 125:119-152

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Hierarchical Clustering: A 0.585 Revenue Approximation

Noga Alon, Yossi Azar, Danny Vainstein ; PMLR 125:153-162

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Winnowing with Gradient Descent

Ehsan Amid, Manfred K. Warmuth ; PMLR 125:163-182

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Pan-Private Uniformity Testing

Kareem Amin, Matthew Joseph, Jieming Mao ; PMLR 125:183-218

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Dimension-Free Bounds for Chasing Convex Functions

C.J. Argue, Anupam Gupta, Guru Guruganesh ; PMLR 125:219-241

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Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations

Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan ; PMLR 125:242-299

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Data-driven confidence bands for distributed nonparametric regression

Valeriy Avanesov ; PMLR 125:300-322

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Estimating Principal Components under Adversarial Perturbations

Pranjal Awasthi, Xue Chen, Aravindan Vijayaraghavan ; PMLR 125:323-362

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Active Local Learning

Arturs Backurs, Avrim Blum, Neha Gupta ; PMLR 125:363-390

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Finite Regret and Cycles with Fixed Step-Size via Alternating Gradient Descent-Ascent

James P. Bailey, Gauthier Gidel, Georgios Piliouras ; PMLR 125:391-407

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Calibrated Surrogate Losses for Adversarially Robust Classification

Han Bao, Clay Scott, Masashi Sugiyama ; PMLR 125:408-451

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Complexity Guarantees for Polyak Steps with Momentum

Mathieu Barré, Adrien Taylor, Alexandre d’Aspremont ; PMLR 125:452-478

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Free Energy Wells and Overlap Gap Property in Sparse PCA

Gérard Ben Arous, Alexander S. Wein, Ilias Zadik ; PMLR 125:479-482

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Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process

Guy Blanc, Neha Gupta, Gregory Valiant, Paul Valiant ; PMLR 125:483-513

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Hardness of Identity Testing for Restricted Boltzmann Machines and Potts models

Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda ; PMLR 125:514-529

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Selfish Robustness and Equilibria in Multi-Player Bandits

Etienne Boursier, Vianney Perchet ; PMLR 125:530-581

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Proper Learning, Helly Number, and an Optimal SVM Bound

Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy ; PMLR 125:582-609

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Sharper Bounds for Uniformly Stable Algorithms

Olivier Bousquet, Yegor Klochkov, Nikita Zhivotovskiy ; PMLR 125:610-626

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The Gradient Complexity of Linear Regression

Mark Braverman, Elad Hazan, Max Simchowitz, Blake Woodworth ; PMLR 125:627-647

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Reducibility and Statistical-Computational Gaps from Secret Leakage

Matthew Brennan, Guy Bresler ; PMLR 125:648-847

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A Corrective View of Neural Networks: Representation, Memorization and Learning

Guy Bresler, Dheeraj Nagaraj ; PMLR 125:848-901

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ID3 Learns Juntas for Smoothed Product Distributions

Alon Brutzkus, Amit Daniely, Eran Malach ; PMLR 125:902-915

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Coordination without communication: optimal regret in two players multi-armed bandits

Sébastien Bubeck, Thomas Budzinski ; PMLR 125:916-939

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How to Trap a Gradient Flow

Sébastien Bubeck, Dan Mikulincer ; PMLR 125:940-960

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Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without

Sébastien Bubeck, Yuanzhi Li, Yuval Peres, Mark Sellke ; PMLR 125:961-987

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Highly smooth minimization of non-smooth problems

Brian Bullins ; PMLR 125:988-1030

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Efficient, Noise-Tolerant, and Private Learning via Boosting

Mark Bun, Marco Leandro Carmosino, Jessica Sorrell ; PMLR 125:1031-1077

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The estimation error of general first order methods

Michael Celentano, Andrea Montanari, Yuchen Wu ; PMLR 125:1078-1141

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Bounds in query learning

Hunter Chase, James Freitag ; PMLR 125:1142-1160

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Learning Polynomials in Few Relevant Dimensions

Sitan Chen, Raghu Meka ; PMLR 125:1161-1227

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The Influence of Shape Constraints on the Thresholding Bandit Problem

James Cheshire, Pierre Menard, Alexandra Carpentier ; PMLR 125:1228-1275

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Gradient descent algorithms for Bures-Wasserstein barycenters

Sinho Chewi, Tyler Maunu, Philippe Rigollet, Austin J. Stromme ; PMLR 125:1276-1304

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Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss

Lénaïc Chizat, Francis Bach ; PMLR 125:1305-1338

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ODE-Inspired Analysis for the Biological Version of Oja’s Rule in Solving Streaming PCA

Chi-Ning Chou, Mien Brabeeba Wang ; PMLR 125:1339-1343

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Pessimism About Unknown Unknowns Inspires Conservatism

Michael K. Cohen, Marcus Hutter ; PMLR 125:1344-1373

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Optimal Group Testing

Amin Coja-Oghlan, Oliver Gebhard, Max Hahn-Klimroth, Philipp Loick ; PMLR 125:1374-1388

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PAC learning with stable and private predictions

Yuval Dagan, Vitaly Feldman ; PMLR 125:1389-1410

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High probability guarantees for stochastic convex optimization

Damek Davis, Dmitriy Drusvyatskiy ; PMLR 125:1411-1427

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Halpern Iteration for Near-Optimal and Parameter-Free Monotone Inclusion and Strong Solutions to Variational Inequalities

Jelena Diakonikolas ; PMLR 125:1428-1451

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Approximation Schemes for ReLU Regression

Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi ; PMLR 125:1452-1485

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Learning Halfspaces with Massart Noise Under Structured Distributions

Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis ; PMLR 125:1486-1513

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Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks

Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Nikos Zarifis ; PMLR 125:1514-1539

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Consistent recovery threshold of hidden nearest neighbor graphs

Jian Ding, Yihong Wu, Jiaming Xu, Dana Yang ; PMLR 125:1540-1553

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Root-n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank

Kefan Dong, Jian Peng, Yining Wang, Yuan Zhou ; PMLR 125:1554-1557

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Embedding Dimension of Polyhedral Losses

Jessie Finocchiaro, Rafael Frongillo, Bo Waggoner ; PMLR 125:1558-1585

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Efficient Parameter Estimation of Truncated Boolean Product Distributions

Dimitris Fotakis, Alkis Kalavasis, Christos Tzamos ; PMLR 125:1586-1600

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Rigorous Guarantees for Tyler’s M-Estimator via Quantum Expansion

William Cole Franks, Ankur Moitra ; PMLR 125:1601-1632

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From tree matching to sparse graph alignment

Luca Ganassali, Laurent Massoulié ; PMLR 125:1633-1665

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On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems

Dan Garber ; PMLR 125:1666-1681

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Asymptotic Errors for High-Dimensional Convex Penalized Linear Regression beyond Gaussian Matrices

Cédric Gerbelot, Alia Abbara, Florent Krzakala ; PMLR 125:1682-1713

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No-Regret Prediction in Marginally Stable Systems

Udaya Ghai, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang ; PMLR 125:1714-1757

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Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems

Noah Golowich, Sarath Pattathil, Constantinos Daskalakis, Asuman Ozdaglar ; PMLR 125:1758-1784

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Locally Private Hypothesis Selection

Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang ; PMLR 125:1785-1816

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Bessel Smoothing and Multi-Distribution Property Estimation

Yi Hao, Ping Li ; PMLR 125:1817-1876

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Faster Projection-free Online Learning

Elad Hazan, Edgar Minasyan ; PMLR 125:1877-1893

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Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond

Oliver Hinder, Aaron Sidford, Nimit Sohoni ; PMLR 125:1894-1938

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A Greedy Anytime Algorithm for Sparse PCA

Guy Holtzman, Adam Soffer, Dan Vilenchik ; PMLR 125:1939-1956

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Noise-tolerant, Reliable Active Classification with Comparison Queries

Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan ; PMLR 125:1957-2006

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Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes

Yichun Hu, Nathan Kallus, Xiaojie Mao ; PMLR 125:2007-2010

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Extrapolating the profile of a finite population

Soham Jana, Yury Polyanskiy, Yihong Wu ; PMLR 125:2011-2033

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Precise Tradeoffs in Adversarial Training for Linear Regression

Adel Javanmard, Mahdi Soltanolkotabi, Hamed Hassani ; PMLR 125:2034-2078

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Robust causal inference under covariate shift via worst-case subpopulation treatment effects

Sookyo Jeong, Hongseok Namkoong ; PMLR 125:2079-2084

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Efficient improper learning for online logistic regression

Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi ; PMLR 125:2085-2108

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Gradient descent follows the regularization path for general losses

Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky ; PMLR 125:2109-2136

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Provably efficient reinforcement learning with linear function approximation

Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I Jordan ; PMLR 125:2137-2143

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Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise

Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai ; PMLR 125:2144-2203

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Private Mean Estimation of Heavy-Tailed Distributions

Gautam Kamath, Vikrant Singhal, Jonathan Ullman ; PMLR 125:2204-2235

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Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity

Pritish Kamath, Omar Montasser, Nathan Srebro ; PMLR 125:2236-2262

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Privately Learning Thresholds: Closing the Exponential Gap

Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer ; PMLR 125:2263-2285

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Online Learning with Vector Costs and Bandits with Knapsacks

Thomas Kesselheim, Sahil Singla ; PMLR 125:2286-2305

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Universal Approximation with Deep Narrow Networks

Patrick Kidger, Terry Lyons ; PMLR 125:2306-2327

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Information Directed Sampling for Linear Partial Monitoring

Johannes Kirschner, Tor Lattimore, Andreas Krause ; PMLR 125:2328-2369

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New Potential-Based Bounds for Prediction with Expert Advice

Vladimir A. Kobzar, Robert V. Kohn, Zhilei Wang ; PMLR 125:2370-2405

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On Suboptimality of Least Squares with Application to Estimation of Convex Bodies

Gil Kur, Alexander Rakhlin, Adityanand Guntuboyina ; PMLR 125:2406-2424

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The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians

Jeongyeol Kwon, Constantine Caramanis ; PMLR 125:2425-2487

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Exploration by Optimisation in Partial Monitoring

Tor Lattimore, Csaba Szespvári ; PMLR 125:2488-2515

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A Closer Look at Small-loss Bounds for Bandits with Graph Feedback

Chung-Wei Lee, Haipeng Luo, Mengxiao Zhang ; PMLR 125:2516-2564

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Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo

Yin Tat Lee, Ruoqi Shen, Kevin Tian ; PMLR 125:2565-2597

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A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates

Zhixian Lei, Kyle Luh, Prayaag Venkat, Fred Zhang ; PMLR 125:2598-2612

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Learning Over-Parametrized Two-Layer Neural Networks beyond NTK

Yuanzhi Li, Tengyu Ma, Hongyang R. Zhang ; PMLR 125:2613-2682

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On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels

Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai ; PMLR 125:2683-2711

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Learning Entangled Single-Sample Gaussians in the Subset-of-Signals Model

Yingyu Liang, Hui Yuan ; PMLR 125:2712-2737

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Near-Optimal Algorithms for Minimax Optimization

Tianyi Lin, Chi Jin, Michael I. Jordan ; PMLR 125:2738-2779

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Better Algorithms for Estimating Non-Parametric Models in Crowd-Sourcing and Rank Aggregation

Allen Liu, Ankur Moitra ; PMLR 125:2780-2829

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Tight Lower Bounds for Combinatorial Multi-Armed Bandits

Nadav Merlis, Shie Mannor ; PMLR 125:2830-2857

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Lipschitz and Comparator-Norm Adaptivity in Online Learning

Zakaria Mhammedi, Wouter M. Koolen ; PMLR 125:2858-2887

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Information Theoretic Optimal Learning of Gaussian Graphical Models

Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov ; PMLR 125:2888-2909

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Parallels Between Phase Transitions and Circuit Complexity?

Ankur Moitra, Elchanan Mossel, Colin Sandon ; PMLR 125:2910-2946

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On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration

Wenlong Mou, Chris Junchi Li, Martin J Wainwright, Peter L Bartlett, Michael I Jordan ; PMLR 125:2947-2997

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Extending Learnability to Auxiliary-Input Cryptographic Primitives and Meta-PAC Learning

Mikito Nanashima ; PMLR 125:2998-3029

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Fast Rates for Online Prediction with Abstention

Gergely Neu, Nikita Zhivotovskiy ; PMLR 125:3030-3048

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Efficient and robust algorithms for adversarial linear contextual bandits

Gergely Neu, Julia Olkhovskaya ; PMLR 125:3049-3068

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An $\widetilde\mathcal{O}(m/\varepsilon^3.5)$-Cost Algorithm for Semidefinite Programs with Diagonal Constraints

Yin Tat Lee, Swati Padmanabhan ; PMLR 125:3069-3119

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Costly Zero Order Oracles

Renato Paes Leme, Jon Schneider ; PMLR 125:3120-3132

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Adaptive Submodular Maximization under Stochastic Item Costs

Srinivasan Parthasarathy ; PMLR 125:3133-3151

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Covariance-adapting algorithm for semi-bandits with application to sparse outcomes

Pierre Perrault, Michal Valko, Vianney Perchet ; PMLR 125:3152-3184

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Finite-Time Analysis of Asynchronous Stochastic Approximation and $Q$-Learning

Guannan Qu, Adam Wierman ; PMLR 125:3185-3205

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List Decodable Subspace Recovery

Prasad Raghavendra, Morris Yau ; PMLR 125:3206-3226

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Tsallis-INF for Decoupled Exploration and Exploitation in Multi-armed Bandits

Chloé Rouyer, Yevgeny Seldin ; PMLR 125:3227-3249

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How Good is SGD with Random Shuffling?

Itay Safran, Ohad Shamir ; PMLR 125:3250-3284

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A Nearly Optimal Variant of the Perceptron Algorithm for the Uniform Distribution on the Unit Sphere

Marco Schmalhofer ; PMLR 125:3285-3295

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Logistic Regression Regret: What’s the Catch?

Gil I Shamir ; PMLR 125:3296-3319

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Improper Learning for Non-Stochastic Control

Max Simchowitz, Karan Singh, Elad Hazan ; PMLR 125:3320-3436

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Reasoning About Generalization via Conditional Mutual Information

Thomas Steinke, Lydia Zakynthinou ; PMLR 125:3437-3452

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Estimation and Inference with Trees and Forests in High Dimensions

Vasilis Syrgkanis, Manolis Zampetakis ; PMLR 125:3453-3454

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Balancing Gaussian vectors in high dimension

Paxton Turner, Raghu Meka, Philippe Rigollet ; PMLR 125:3455-3486

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Active Learning for Identification of Linear Dynamical Systems

Andrew Wagenmaker, Kevin Jamieson ; PMLR 125:3487-3582

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Taking a hint: How to leverage loss predictors in contextual bandits?

Chen-Yu Wei, Haipeng Luo, Alekh Agarwal ; PMLR 125:3583-3634

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Kernel and Rich Regimes in Overparametrized Models

Blake Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro ; PMLR 125:3635-3673

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Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium

Qiaomin Xie, Yudong Chen, Zhaoran Wang, Zhuoran Yang ; PMLR 125:3674-3682

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Tree-projected gradient descent for estimating gradient-sparse parameters on graphs

Sheng Xu, Zhou Fan, Sahand Negahban ; PMLR 125:3683-3708

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Non-asymptotic Analysis for Nonparametric Testing

Yun Yang, Zuofeng Shang, Guang Cheng ; PMLR 125:3709-3755

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Learning a Single Neuron with Gradient Methods

Gilad Yehudai, Shamir Ohad ; PMLR 125:3756-3786

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Nearly Non-Expansive Bounds for Mahalanobis Hard Thresholding

Xiao-Tong Yuan, Ping Li ; PMLR 125:3787-3813

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Wasserstein Control of Mirror Langevin Monte Carlo

Kelvin Shuangjian Zhang, Gabriel Peyré, Jalal Fadili, Marcelo Pereyra ; PMLR 125:3814-3841

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Open Problem: Model Selection for Contextual Bandits

Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo ; PMLR 125:3842-3846

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Open Problem: Tight Convergence of SGD in Constant Dimension

Tomer Koren, Shahar Segal ; PMLR 125:3847-3851

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Open Problem: Average-Case Hardness of Hypergraphic Planted Clique Detection

Yuetian Luo, Anru R Zhang ; PMLR 125:3852-3856

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Open Problem: Information Complexity of VC Learning

Thomas Steinke, Lydia Zakynthinou ; PMLR 125:3857-3863

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Open Problem: Fast and Optimal Online Portfolio Selection

Tim Van Erven, Dirk Van der Hoeven, Wojciech Kotłowski, Wouter M. Koolen ; PMLR 125:3864-3869

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