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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

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Volume 65: Conference on Learning Theory, 7-10 July 2017, Amsterdam, Netherlands

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Editors: Satyen Kale, Ohad Shamir

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Preface: Conference on Learning Theory (COLT), 2017

; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1-3

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Open Problem: First-Order Regret Bounds for Contextual Bandits

Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Schapire Robert E.; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:4-7

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Open Problem: Meeting Times for Learning Random Automata

Benjamin Fish, Lev Reyzin; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:8-11

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Corralling a Band of Bandit Algorithms

Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:12-38

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Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons

Arpit Agarwal, Shivani Agarwal, Sepehr Assadi, Sanjeev Khanna; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:39-75

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Thompson Sampling for the MNL-Bandit

Shipra Agrawal, Vashist Avadhanula, Vineet Goyal, Assaf Zeevi; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:76-78

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Homotopy Analysis for Tensor PCA

Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:79-104

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Correspondence retrieval

Alexandr Andoni, Daniel Hsu, Kevin Shi, Xiaorui Sun; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:105-126

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Efficient PAC Learning from the Crowd

Pranjal Awasthi, Avrim Blum, Nika Haghtalab, Yishay Mansour; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:127-150

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The Price of Selection in Differential Privacy

Mitali Bafna, Jonathan Ullman; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:151-168

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Computationally Efficient Robust Sparse Estimation in High Dimensions

Sivaraman Balakrishnan, Simon S. Du, Jerry Li, Aarti Singh; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:169-212

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Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems

Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik, Colin White; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:213-274

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The Sample Complexity of Optimizing a Convex Function

Eric Balkanski, Yaron Singer; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:275-301

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Efficient Co-Training of Linear Separators under Weak Dependence

Avrim Blum, Yishay Mansour; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:302-318

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Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo

Nicolas Brosse, Alain Durmus, Éric Moulines, Marcelo Pereyra; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:319-342

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Rates of estimation for determinantal point processes

Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet, John Urschel; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:343-345

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Learning Disjunctions of Predicates

Nader H. Bshouty, Dana Drachsler-Cohen, Martin Vechev, Eran Yahav; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:346-369

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Testing Bayesian Networks

Clement L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:370-448

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Multi-Observation Elicitation

Sebastian Casalaina-Martin, Rafael Frongillo, Tom Morgan, Bo Waggoner; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:449-464

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Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning

Nicolò Cesa-Bianchi, Pierre Gaillard, Claudio Gentile, Sébastien Gerchinovitz; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:465-481

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Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration

Lijie Chen, Anupam Gupta, Jian Li, Mingda Qiao, Ruosong Wang; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:482-534

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Towards Instance Optimal Bounds for Best Arm Identification

Lijie Chen, Jian Li, Mingda Qiao; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:535-592

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Thresholding Based Outlier Robust PCA

Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:593-628

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Tight Bounds for Bandit Combinatorial Optimization

Alon Cohen, Tamir Hazan, Tomer Koren; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:629-642

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Online Learning Without Prior Information

Ashok Cutkosky, Kwabena Boahen; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:643-677

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Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent

Arnak Dalalyan; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:678-689

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Depth Separation for Neural Networks

Amit Daniely; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:690-696

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Square Hellinger Subadditivity for Bayesian Networks and its Applications to Identity Testing

Constantinos Daskalakis, Qinxuan Pan; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:697-703

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Ten Steps of EM Suffice for Mixtures of Two Gaussians

Constantinos Daskalakis, Christos Tzamos, Manolis Zampetakis; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:704-710

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Learning Multivariate Log-concave Distributions

Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:711-727

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Generalization for Adaptively-chosen Estimators via Stable Median

Vitaly Feldman, Thomas Steinke; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:728-757

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Greed Is Good: Near-Optimal Submodular Maximization via Greedy Optimization

Moran Feldman, Christopher Harshaw, Amin Karbasi; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:758-784

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A General Characterization of the Statistical Query Complexity

Vitaly Feldman; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:785-830

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Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$

Nicolas Flammarion, Francis Bach; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:831-875

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ZigZag: A New Approach to Adaptive Online Learning

Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:876-924

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Memoryless Sequences for Differentiable Losses

Rafael Frongillo, Andrew Nobel; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:925-939

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Matrix Completion from $O(n)$ Samples in Linear Time

David Gamarnik, Quan Li, Hongyi Zhang; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:940-947

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High Dimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transtition

Gamarnik David, Zadik Ilias; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:948-953

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Two-Sample Tests for Large Random Graphs Using Network Statistics

Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier, Ulrike von Luxburg; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:954-977

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Effective Semisupervised Learning on Manifolds

Amir Globerson, Roi Livni, Shai Shalev-Shwartz; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:978-1003

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Reliably Learning the ReLU in Polynomial Time

Surbhi Goel, Varun Kanade, Adam Klivans, Justin Thaler; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1004-1042

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Fast Rates for Empirical Risk Minimization of Strict Saddle Problems

Alon Gonen, Shai Shalev-Shwartz; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1043-1063

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Nearly-tight VC-dimension bounds for piecewise linear neural networks

Nick Harvey, Christopher Liaw, Abbas Mehrabian; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1064-1068

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Submodular Optimization under Noise

Avinatan Hassidim, Yaron Singer; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1069-1122

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Surprising properties of dropout in deep networks

David P. Helmbold, Philip M. Long; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1123-1146

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Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes

Lunjia Hu, Ruihan Wu, Tianhong Li, Liwei Wang; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1147-1156

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A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints

Bin Hu, Peter Seiler, Anders Rantzer; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1157-1189

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The Hidden Hubs Problem

Ravindran Kannan, Santosh Vempala; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1190-1213

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Predicting with Distributions

Michael Kearns, Zhiwei Steven Wu; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1214-1241

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Bandits with Movement Costs and Adaptive Pricing

Tomer Koren, Roi Livni, Yishay Mansour; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1242-1268

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Sparse Stochastic Bandits

Joon Kwon, Vianney Perchet, Claire Vernade; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1269-1270

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On the Ability of Neural Nets to Express Distributions

Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski, Sanjeev Arora; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1271-1296

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Fundamental limits of symmetric low-rank matrix estimation

Marc Lelarge, Léo Miolane; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1297-1301

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Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities

Jerry Li, Ludwig Schmidt; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1302-1382

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Adaptivity to Noise Parameters in Nonparametric Active Learning

Carpentier Alexandra Locatelli Andrea, Kpotufe Samory; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1383-1416

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Noisy Population Recovery from Unknown Noise

Shachar Lovett, Jiapeng Zhang; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1417-1431

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Inapproximability of VC Dimension and Littlestone’s Dimension

Pasin Manurangsi, Aviad Rubinstein; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1432-1460

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A Second-order Look at Stability and Generalization

Andreas Maurer; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1461-1475

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Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality

Song Mei, Theodor Misiakiewicz, Andrea Montanari, Roberto Imbuzeiro Oliveira; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1476-1515

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Mixing Implies Lower Bounds for Space Bounded Learning

Dana Moshkovitz, Michal Moshkovitz; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1516-1566

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Fast rates for online learning in Linearly Solvable Markov Decision Processes

Gergely Neu, Vicenç Gómez; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1567-1588

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Sample complexity of population recovery

Yury Polyanskiy, Ananda Theertha Suresh, Yihong Wu; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1589-1618

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Exact tensor completion with sum-of-squares

Aaron Potechin, David Steurer; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1619-1673

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Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis

Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1674-1703

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On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities

Alexander Rakhlin, Karthik Sridharan; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1704-1722

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Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1723-1742

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An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits

Yevgeny Seldin, Gábor Lugosi; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1743-1759

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Fast and robust tensor decomposition with applications to dictionary learning

Tselil Schramm, David Steurer; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1760-1793

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The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime

Max Simchowitz, Kevin Jamieson, Benjamin Recht; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1794-1834

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On Learning vs. Refutation

Salil Vadhan; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1835-1848

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Ignoring Is a Bliss: Learning with Large Noise Through Reweighting-Minimization

Daniel Vainsencher, Shie Mannor, Huan Xu; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1849-1881

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Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox

Jialei Wang, Weiran Wang, Nathan Srebro; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1882-1919

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Learning Non-Discriminatory Predictors

Blake Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1920-1953

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Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds

Lijun Zhang, Tianbao Yang, Rong Jin; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1954-1979

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A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics

Yuchen Zhang, Percy Liang, Moses Charikar; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:1980-2022

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Optimal learning via local entropies and sample compression

Zhivotovskiy Nikita; Proceedings of the 2017 Conference on Learning Theory, PMLR 65:2023-2065

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