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Proceedings of Machine Learning Research

Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research
Proceedings of Machine Learning Research
PMLR · 2026-06-02 · via Proceedings of Machine Learning Research

[edit]

Volume 75: Conference On Learning Theory, 6-9 July 2018,

[edit]

Editors: Sébastien Bubeck, Vianney Perchet, Philippe Rigollet

[bib][citeproc]

Contents:

  • Preface
  • Best Paper Awards
  • Regular Papers
  • Open Problems

Filter Authors: Filter Titles:

Preface

Conference on Learning Theory 2018: Preface

; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1-1

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Best Paper Awards

Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations

Yuanzhi Li, Tengyu Ma, Hongyang Zhang; Proceedings of the 31st Conference On Learning Theory, PMLR 75:2-47

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Reducibility and Computational Lower Bounds for Problems with Planted Sparse Structure

Matthew Brennan, Guy Bresler, Wasim Huleihel; Proceedings of the 31st Conference On Learning Theory, PMLR 75:48-166

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Logistic Regression: The Importance of Being Improper

Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan; Proceedings of the 31st Conference On Learning Theory, PMLR 75:167-208

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Regular Papers

Actively Avoiding Nonsense in Generative Models

Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, Christos Tzamos; Proceedings of the 31st Conference On Learning Theory, PMLR 75:209-227

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A Faster Approximation Algorithm for the Gibbs Partition Function

Vladimir Kolmogorov; Proceedings of the 31st Conference On Learning Theory, PMLR 75:228-249

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Exponential Convergence of Testing Error for Stochastic Gradient Methods

Loucas Pillaud-Vivien, Alessandro Rudi, Francis Bach; Proceedings of the 31st Conference On Learning Theory, PMLR 75:250-296

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Size-Independent Sample Complexity of Neural Networks

Noah Golowich, Alexander Rakhlin, Ohad Shamir; Proceedings of the 31st Conference On Learning Theory, PMLR 75:297-299

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Underdamped Langevin MCMC: A non-asymptotic analysis

Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan; Proceedings of the 31st Conference On Learning Theory, PMLR 75:300-323

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Online Variance Reduction for Stochastic Optimization

Zalan Borsos, Andreas Krause, Kfir Y. Levy; Proceedings of the 31st Conference On Learning Theory, PMLR 75:324-357

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Information Directed Sampling and Bandits with Heteroscedastic Noise

Johannes Kirschner, Andreas Krause; Proceedings of the 31st Conference On Learning Theory, PMLR 75:358-384

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Testing Symmetric Markov Chains From a Single Trajectory

Constantinos Daskalakis, Nishanth Dikkala, Nick Gravin; Proceedings of the 31st Conference On Learning Theory, PMLR 75:385-409

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Detection limits in the high-dimensional spiked rectangular model

Ahmed El Alaoui, Michael I. Jordan; Proceedings of the 31st Conference On Learning Theory, PMLR 75:410-438

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Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification

Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht; Proceedings of the 31st Conference On Learning Theory, PMLR 75:439-473

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Active Tolerant Testing

Avrim Blum, Lunjia Hu; Proceedings of the 31st Conference On Learning Theory, PMLR 75:474-497

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Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods

Yan Shuo Tan, Roman Vershynin; Proceedings of the 31st Conference On Learning Theory, PMLR 75:498-534

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Calibrating Noise to Variance in Adaptive Data Analysis

Vitaly Feldman, Thomas Steinke; Proceedings of the 31st Conference On Learning Theory, PMLR 75:535-544

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Accelerating Stochastic Gradient Descent for Least Squares Regression

Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; Proceedings of the 31st Conference On Learning Theory, PMLR 75:545-604

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Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints

Wenlong Mou, Liwei Wang, Xiyu Zhai, Kai Zheng; Proceedings of the 31st Conference On Learning Theory, PMLR 75:605-638

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Optimal approximation of continuous functions by very deep ReLU networks

Dmitry Yarotsky; Proceedings of the 31st Conference On Learning Theory, PMLR 75:639-649

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Averaging Stochastic Gradient Descent on Riemannian Manifolds

Nilesh Tripuraneni, Nicolas Flammarion, Francis Bach, Michael I. Jordan; Proceedings of the 31st Conference On Learning Theory, PMLR 75:650-687

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Fitting a Putative Manifold to Noisy Data

Charles Fefferman, Sergei Ivanov, Yaroslav Kurylev, Matti Lassas, Hariharan Narayanan; Proceedings of the 31st Conference On Learning Theory, PMLR 75:688-720

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Private Sequential Learning

John Tsitsiklis, Kuang Xu, Zhi Xu; Proceedings of the 31st Conference On Learning Theory, PMLR 75:721-727

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Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models

Jean Barbier, Florent Krzakala, Nicolas Macris, Léo Miolane, Lenka Zdeborová; Proceedings of the 31st Conference On Learning Theory, PMLR 75:728-731

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Exact and Robust Conformal Inference Methods for Predictive Machine Learning with Dependent Data

Victor Chernozhukov, Kaspar Wüthrich, Zhu Yinchu; Proceedings of the 31st Conference On Learning Theory, PMLR 75:732-749

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Nonstochastic Bandits with Composite Anonymous Feedback

Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour; Proceedings of the 31st Conference On Learning Theory, PMLR 75:750-773

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Lower Bounds for Higher-Order Convex Optimization

Naman Agarwal, Elad Hazan; Proceedings of the 31st Conference On Learning Theory, PMLR 75:774-792

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Log-concave sampling: Metropolis-Hastings algorithms are fast!

Raaz Dwivedi, Yuansi Chen, Martin J Wainwright, Bin Yu; Proceedings of the 31st Conference On Learning Theory, PMLR 75:793-797

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Incentivizing Exploration by Heterogeneous Users

Bangrui Chen, Peter Frazier, David Kempe; Proceedings of the 31st Conference On Learning Theory, PMLR 75:798-818

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Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms

Ilias Diakonikolas, Jerry Li, Ludwig Schmidt; Proceedings of the 31st Conference On Learning Theory, PMLR 75:819-842

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Time-Space Tradeoffs for Learning Finite Functions from Random Evaluations, with Applications to Polynomials

Paul Beame, Shayan Oveis Gharan, Xin Yang; Proceedings of the 31st Conference On Learning Theory, PMLR 75:843-856

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Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability

Belinda Tzen, Tengyuan Liang, Maxim Raginsky; Proceedings of the 31st Conference On Learning Theory, PMLR 75:857-875

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Hardness of Learning Noisy Halfspaces using Polynomial Thresholds

Arnab Bhattacharyya, Suprovat Ghoshal, Rishi Saket; Proceedings of the 31st Conference On Learning Theory, PMLR 75:876-917

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Best of both worlds: Stochastic & adversarial best-arm identification

Yasin Abbasi-Yadkori, Peter Bartlett, Victor Gabillon, Alan Malek, Michal Valko; Proceedings of the 31st Conference On Learning Theory, PMLR 75:918-949

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Learning Patterns for Detection with Multiscale Scan Statistics

James Sharpnack; Proceedings of the 31st Conference On Learning Theory, PMLR 75:950-969

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Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk

Paul Hand, Vladislav Voroninski; Proceedings of the 31st Conference On Learning Theory, PMLR 75:970-978

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Small-loss bounds for online learning with partial information

Thodoris Lykouris, Karthik Sridharan, Éva Tardos; Proceedings of the 31st Conference On Learning Theory, PMLR 75:979-986

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Empirical bounds for functions with weak interactions

Andreas Maurer, Massimiliano Pontil; Proceedings of the 31st Conference On Learning Theory, PMLR 75:987-1010

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Restricted Eigenvalue from Stable Rank with Applications to Sparse Linear Regression

Shiva Prasad Kasiviswanathan, Mark Rudelson; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1011-1041

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Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent

Chi Jin, Praneeth Netrapalli, Michael I. Jordan; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1042-1085

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Convex Optimization with Unbounded Nonconvex Oracles using Simulated Annealing

Oren Mangoubi, Nisheeth K. Vishnoi; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1086-1124

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Learning Mixtures of Linear Regressions with Nearly Optimal Complexity

Yuanzhi Li, Yingyu Liang; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1125-1144

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Detecting Correlations with Little Memory and Communication

Yuval Dagan, Ohad Shamir; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1145-1198

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Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning

Gal Dalal, Gugan Thoppe, Balázs Szörényi, Shie Mannor; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1199-1233

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Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of Multivariate Log-concave Densities

Timothy Carpenter, Ilias Diakonikolas, Anastasios Sidiropoulos, Alistair Stewart; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1234-1262

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More Adaptive Algorithms for Adversarial Bandits

Chen-Yu Wei, Haipeng Luo; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1263-1291

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Efficient Convex Optimization with Membership Oracles

Yin Tat Lee, Aaron Sidford, Santosh S. Vempala; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1292-1294

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A General Approach to Multi-Armed Bandits Under Risk Criteria

Asaf Cassel, Shie Mannor, Assaf Zeevi; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1295-1306

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An Optimal Learning Algorithm for Online Unconstrained Submodular Maximization

Tim Roughgarden, Joshua R. Wang; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1307-1325

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The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity

Vishesh Jain, Frederic Koehler, Elchanan Mossel; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1326-1347

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Approximation beats concentration? An approximation view on inference with smooth radial kernels

Mikhail Belkin; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1348-1361

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Non-Convex Matrix Completion Against a Semi-Random Adversary

Yu Cheng, Rong Ge; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1362-1394

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The Vertex Sample Complexity of Free Energy is Polynomial

Vishesh Jain, Frederic Koehler, Elchanan Mossel; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1395-1419

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Efficient Algorithms for Outlier-Robust Regression

Adam Klivans, Pravesh K. Kothari, Raghu Meka; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1420-1430

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Action-Constrained Markov Decision Processes With Kullback-Leibler Cost

Ana Bušić, Sean Meyn; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1431-1444

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Fundamental Limits of Weak Recovery with Applications to Phase Retrieval

Marco Mondelli, Andrea Montanari; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1445-1450

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Cutting plane methods can be extended into nonconvex optimization

Oliver Hinder; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1451-1454

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An Analysis of the t-SNE Algorithm for Data Visualization

Sanjeev Arora, Wei Hu, Pravesh K. Kothari; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1455-1462

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Adaptivity to Smoothness in X-armed bandits

Andrea Locatelli, Alexandra Carpentier; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1463-1492

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Black-Box Reductions for Parameter-free Online Learning in Banach Spaces

Ashok Cutkosky, Francesco Orabona; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1493-1529

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A Data Prism: Semi-verified learning in the small-alpha regime

Michela Meister, Gregory Valiant; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1530-1546

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A Direct Sum Result for the Information Complexity of Learning

Ido Nachum, Jonathan Shafer, Amir Yehudayoff; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1547-1568

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Online learning over a finite action set with limited switching

Jason Altschuler, Kunal Talwar; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1569-1573

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Smoothed Online Convex Optimization in High Dimensions via Online Balanced Descent

Niangjun Chen, Gautam Goel, Adam Wierman; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1574-1594

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Faster Rates for Convex-Concave Games

Jacob Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1595-1625

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$\ell_1$ Regression using Lewis Weights Preconditioning and Stochastic Gradient Descent

David Durfee, Kevin A. Lai, Saurabh Sawlani; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1626-1656

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Optimal Single Sample Tests for Structured versus Unstructured Network Data

Guy Bresler, Dheeraj Nagaraj; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1657-1690

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A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation

Jalaj Bhandari, Daniel Russo, Raghav Singal; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1691-1692

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Privacy-preserving Prediction

Cynthia Dwork, Vitaly Feldman; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1693-1702

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An Estimate Sequence for Geodesically Convex Optimization

Hongyi Zhang, Suvrit Sra; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1703-1723

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The Externalities of Exploration and How Data Diversity Helps Exploitation

Manish Raghavan, Aleksandrs Slivkins, Jennifer Vaughan Wortman, Zhiwei Steven Wu; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1724-1738

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Efficient Contextual Bandits in Non-stationary Worlds

Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1739-1776

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Langevin Monte Carlo and JKO splitting

Espen Bernton; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1777-1798

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Subpolynomial trace reconstruction for random strings \{and arbitrary deletion probability

Nina Holden, Robin Pemantle, Yuval Peres; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1799-1840

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An explicit analysis of the entropic penalty in linear programming

Jonathan Weed; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1841-1855

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Efficient active learning of sparse halfspaces

Chicheng Zhang; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1856-1880

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Marginal Singularity, and the Benefits of Labels in Covariate-Shift

Samory Kpotufe, Guillaume Martinet; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1882-1886

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Learning Single-Index Models in Gaussian Space

Rishabh Dudeja, Daniel Hsu; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1887-1930

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Hidden Integrality of SDP Relaxations for Sub-Gaussian Mixture Models

Yingjie Fei, Yudong Chen; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1931-1965

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Counting Motifs with Graph Sampling

Jason M. Klusowski, Yihong Wu; Proceedings of the 31st Conference On Learning Theory, PMLR 75:1966-2011

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Approximate Nearest Neighbors in Limited Space

Piotr Indyk, Tal Wagner; Proceedings of the 31st Conference On Learning Theory, PMLR 75:2012-2036

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Breaking the $1/\sqrt{n}$ Barrier: Faster Rates for Permutation-based Models in Polynomial Time

Cheng Mao, Ashwin Pananjady, Martin J. Wainwright; Proceedings of the 31st Conference On Learning Theory, PMLR 75:2037-2042

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Unleashing Linear Optimizers for Group-Fair Learning and Optimization

Daniel Alabi, Nicole Immorlica, Adam Kalai; Proceedings of the 31st Conference On Learning Theory, PMLR 75:2043-2066

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The Many Faces of Exponential Weights in Online Learning

Dirk Hoeven, Tim Erven, Wojciech Kotłowski; Proceedings of the 31st Conference On Learning Theory, PMLR 75:2067-2092

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Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem

Andre Wibisono; Proceedings of the 31st Conference On Learning Theory, PMLR 75:2093-3027

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Online Learning: Sufficient Statistics and the Burkholder Method

Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan; Proceedings of the 31st Conference On Learning Theory, PMLR 75:3028-3064

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Minimax Bounds on Stochastic Batched Convex Optimization

John Duchi, Feng Ruan, Chulhee Yun; Proceedings of the 31st Conference On Learning Theory, PMLR 75:3065-3162

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Geometric Lower Bounds for Distributed Parameter Estimation under Communication Constraints

Yanjun Han, Ayfer Özgür, Tsachy Weissman; Proceedings of the 31st Conference On Learning Theory, PMLR 75:3163-3188

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Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance

Yanjun Han, Jiantao Jiao, Tsachy Weissman; Proceedings of the 31st Conference On Learning Theory, PMLR 75:3189-3221

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Iterate Averaging as Regularization for Stochastic Gradient Descent

Gergely Neu, Lorenzo Rosasco; Proceedings of the 31st Conference On Learning Theory, PMLR 75:3222-3242

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Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form

Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli; Proceedings of the 31st Conference On Learning Theory, PMLR 75:3243-3270

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Certified Computation from Unreliable Datasets

Themis Gouleakis, Christos Tzamos, Manolis Zampetakis; Proceedings of the 31st Conference On Learning Theory, PMLR 75:3271-3294

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Open Problems

Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon

Nan Jiang, Alekh Agarwal; Proceedings of the 31st Conference On Learning Theory, PMLR 75:3395-3398

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Open problem: Improper learning of mixtures of Gaussians

Elad Hazan, Livni Roi; Proceedings of the 31st Conference On Learning Theory, PMLR 75:3399-3402

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