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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 49: Conference on Learning Theory, 23-26 June 2016, Columbia University, New York, New York, USA

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Editors: Vitaly Feldman, Alexander Rakhlin, Ohad Shamir

[bib][citeproc]

Contents:

  • Preface
  • Regular Papers
  • Open Problems

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Preface

Conference on Learning Theory 2016: Preface

; 29th Annual Conference on Learning Theory, PMLR 49:1-3

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Regular Papers

Open Problems

Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies

Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar; 29th Annual Conference on Learning Theory, PMLR 49:1639-1642

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Open Problem: Best Arm Identification: Almost Instance-Wise Optimality and the Gap Entropy Conjecture

Lijie Chen, Jian Li; 29th Annual Conference on Learning Theory, PMLR 49:1643-1646

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Open Problem: Kernel methods on manifolds and metric spaces. What is the probability of a positive definite geodesic exponential kernel?

Aasa Feragen, Søren Hauberg; 29th Annual Conference on Learning Theory, PMLR 49:1647-1650

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Open Problem: Second order regret bounds based on scaling time

Yoav Freund; 29th Annual Conference on Learning Theory, PMLR 49:1651-1654

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Open Problem: Property Elicitation and Elicitation Complexity

Rafael Frongillo, Ian Kash, Stephen Becker; 29th Annual Conference on Learning Theory, PMLR 49:1655-1658

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Open Problem: Parameter-Free and Scale-Free Online Algorithms

Francesco Orabona, Dávid Pál; 29th Annual Conference on Learning Theory, PMLR 49:1659-1664

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An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives

Shipra Agrawal, Nikhil R. Devanur, Lihong Li; 29th Annual Conference on Learning Theory, PMLR 49:4-18

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Learning and Testing Junta Distributions

Maryam Aliakbarpour, Eric Blais, Ronitt Rubinfeld; 29th Annual Conference on Learning Theory, PMLR 49:19-46

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Sign rank versus VC dimension

Noga Alon, Shay Moran, Amir Yehudayoff; 29th Annual Conference on Learning Theory, PMLR 49:47-80

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Efficient approaches for escaping higher order saddle points in non-convex optimization

Animashree Anandkumar, Rong Ge; 29th Annual Conference on Learning Theory, PMLR 49:81-102

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Monte Carlo Markov Chain Algorithms for Sampling Strongly Rayleigh Distributions and Determinantal Point Processes

Nima Anari, Shayan Oveis Gharan, Alireza Rezaei; 29th Annual Conference on Learning Theory, PMLR 49:103-115

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An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits

Peter Auer, Chao-Kai Chiang; 29th Annual Conference on Learning Theory, PMLR 49:116-120

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Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models

Bernardo Ávila Pires, Csaba Szepesvári; 29th Annual Conference on Learning Theory, PMLR 49:121-151

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Learning and 1-bit Compressed Sensing under Asymmetric Noise

Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Hongyang Zhang; 29th Annual Conference on Learning Theory, PMLR 49:152-192

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Reinforcement Learning of POMDPs using Spectral Methods

Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar; 29th Annual Conference on Learning Theory, PMLR 49:193-256

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Highly-Smooth Zero-th Order Online Optimization

Francis Bach, Vianney Perchet; 29th Annual Conference on Learning Theory, PMLR 49:257-283

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An Improved Gap-Dependency Analysis of the Noisy Power Method

Maria-Florina Balcan, Simon Shaolei Du, Yining Wang, Adams Wei Yu; 29th Annual Conference on Learning Theory, PMLR 49:284-309

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Learning Combinatorial Functions from Pairwise Comparisons

Maria-Florina Balcan, Ellen Vitercik, Colin White; 29th Annual Conference on Learning Theory, PMLR 49:310-335

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Instance-dependent Regret Bounds for Dueling Bandits

Akshay Balsubramani, Zohar Karnin, Robert E. Schapire, Masrour Zoghi; 29th Annual Conference on Learning Theory, PMLR 49:336-360

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On the low-rank approach for semidefinite programs arising in synchronization and community detection

Afonso S. Bandeira, Nicolas Boumal, Vladislav Voroninski; 29th Annual Conference on Learning Theory, PMLR 49:361-382

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Information-theoretic thresholds for community detection in sparse networks

Jess Banks, Cristopher Moore, Joe Neeman, Praneeth Netrapalli; 29th Annual Conference on Learning Theory, PMLR 49:383-416

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Noisy Tensor Completion via the Sum-of-Squares Hierarchy

Boaz Barak, Ankur Moitra; 29th Annual Conference on Learning Theory, PMLR 49:417-445

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Basis Learning as an Algorithmic Primitive

Mikhail Belkin, Luis Rademacher, James Voss; 29th Annual Conference on Learning Theory, PMLR 49:446-487

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Aggregation of supports along the Lasso path

Pierre C. Bellec; 29th Annual Conference on Learning Theory, PMLR 49:488-529

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Dropping Convexity for Faster Semi-definite Optimization

Srinadh Bhojanapalli, Anastasios Kyrillidis, Sujay Sanghavi; 29th Annual Conference on Learning Theory, PMLR 49:530-582

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Multi-scale exploration of convex functions and bandit convex optimization

Sébastien Bubeck, Ronen Eldan; 29th Annual Conference on Learning Theory, PMLR 49:583-589

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Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem

Alexandra Carpentier, Andrea Locatelli; 29th Annual Conference on Learning Theory, PMLR 49:590-604

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Delay and Cooperation in Nonstochastic Bandits

Nicol‘o Cesa-Bianchi, Claudio Gentile, Yishay Mansour, Alberto Minora; 29th Annual Conference on Learning Theory, PMLR 49:605-622

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On the Approximability of Sparse PCA

Siu On Chan, Dimitris Papailliopoulos, Aviad Rubinstein; 29th Annual Conference on Learning Theory, PMLR 49:623-646

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Pure Exploration of Multi-armed Bandit Under Matroid Constraints

Lijie Chen, Anupam Gupta, Jian Li; 29th Annual Conference on Learning Theory, PMLR 49:647-669

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Provably manipulation-resistant reputation systems

Paul Christiano; 29th Annual Conference on Learning Theory, PMLR 49:670-697

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On the Expressive Power of Deep Learning: A Tensor Analysis

Nadav Cohen, Or Sharir, Amnon Shashua; 29th Annual Conference on Learning Theory, PMLR 49:698-728

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A Light Touch for Heavily Constrained SGD

Andrew Cotter, Maya Gupta, Jan Pfeifer; 29th Annual Conference on Learning Theory, PMLR 49:729-771

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Adaptive Learning with Robust Generalization Guarantees

Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu; 29th Annual Conference on Learning Theory, PMLR 49:772-814

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Complexity Theoretic Limitations on Learning DNF’s

Amit Daniely, Shai Shalev-Shwartz; 29th Annual Conference on Learning Theory, PMLR 49:815-830

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Optimal Learning via the Fourier Transform for Sums of Independent Integer Random Variables

I. Diakonikolas, D. M. Kane, A. Stewart; 29th Annual Conference on Learning Theory, PMLR 49:831-849

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Properly Learning Poisson Binomial Distributions in Almost Polynomial Time

I. Diakonikolas, D. M. Kane, A. Stewart; 29th Annual Conference on Learning Theory, PMLR 49:850-878

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Asymptotic behavior of \ell_p-based Laplacian regularization in semi-supervised learning

Ahmed El Alaoui, Xiang Cheng, Aaditya Ramdas, Martin J. Wainwright, Michael I. Jordan; 29th Annual Conference on Learning Theory, PMLR 49:879-906

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The Power of Depth for Feedforward Neural Networks

Ronen Eldan, Ohad Shamir; 29th Annual Conference on Learning Theory, PMLR 49:907-940

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Online Learning and Blackwell Approachability in Quitting Games

Janos Flesch, Rida Laraki, Vianney Perchet; 29th Annual Conference on Learning Theory, PMLR 49:941-942

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Spectral thresholds in the bipartite stochastic block model

Laura Florescu, Will Perkins; 29th Annual Conference on Learning Theory, PMLR 49:943-959

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Online Sparse Linear Regression

Dean Foster, Satyen Kale, Howard Karloff; 29th Annual Conference on Learning Theory, PMLR 49:960-970

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Preference-based Teaching

Ziyuan Gao, Christoph Ries, Hans Simon, Sandra Zilles; 29th Annual Conference on Learning Theory, PMLR 49:971-997

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Optimal Best Arm Identification with Fixed Confidence

Aurélien Garivier, Emilie Kaufmann; 29th Annual Conference on Learning Theory, PMLR 49:998-1027

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Maximin Action Identification: A New Bandit Framework for Games

Aurélien Garivier, Emilie Kaufmann, Wouter M. Koolen; 29th Annual Conference on Learning Theory, PMLR 49:1028-1050

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Semidefinite Programs for Exact Recovery of a Hidden Community

Bruce Hajek, Yihong Wu, Jiaming Xu; 29th Annual Conference on Learning Theory, PMLR 49:1051-1095

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Online Learning with Low Rank Experts

Elad Hazan, Tomer Koren, Roi Livni, Yishay Mansour; 29th Annual Conference on Learning Theory, PMLR 49:1096-1114

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Optimal rates for total variation denoising

Jan-Christian Hütter, Philippe Rigollet; 29th Annual Conference on Learning Theory, PMLR 49:1115-1146

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Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja’s Algorithm

Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford; 29th Annual Conference on Learning Theory, PMLR 49:1147-1164

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Online Isotonic Regression

Wojciech Kotłowski, Wouter M. Koolen, Alan Malek; 29th Annual Conference on Learning Theory, PMLR 49:1165-1189

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Time series prediction and online learning

Vitaly Kuznetsov, Mehryar Mohri; 29th Annual Conference on Learning Theory, PMLR 49:1190-1213

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Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits

Tor Lattimore; 29th Annual Conference on Learning Theory, PMLR 49:1214-1245

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Gradient Descent Only Converges to Minimizers

Jason D. Lee, Max Simchowitz, Michael I. Jordan, Benjamin Recht; 29th Annual Conference on Learning Theory, PMLR 49:1246-1257

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Learning Communities in the Presence of Errors

Konstantin Makarychev, Yury Makarychev, Aravindan Vijayaraghavan; 29th Annual Conference on Learning Theory, PMLR 49:1258-1291

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On the capacity of information processing systems

Laurent Massoulie, Kuang Xu; 29th Annual Conference on Learning Theory, PMLR 49:1292-1297

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Learning Simple Auctions

Jamie Morgenstern, Tim Roughgarden; 29th Annual Conference on Learning Theory, PMLR 49:1298-1318

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Density Evolution in the Degree-correlated Stochastic Block Model

Elchanan Mossel, Jiaming Xu; 29th Annual Conference on Learning Theory, PMLR 49:1319-1356

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Cortical Computation via Iterative Constructions

Christos Papadimitriou, Samantha Petti, Santosh Vempala; 29th Annual Conference on Learning Theory, PMLR 49:1357-1375

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When can we rank well from comparisons of O(n\log(n)) non-actively chosen pairs?

Arun Rajkumar, Shivani Agarwal; 29th Annual Conference on Learning Theory, PMLR 49:1376-1401

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How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods

Andrej Risteski; 29th Annual Conference on Learning Theory, PMLR 49:1402-1416

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Simple Bayesian Algorithms for Best Arm Identification

Daniel Russo; 29th Annual Conference on Learning Theory, PMLR 49:1417-1418

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Interactive Algorithms: from Pool to Stream

Sivan Sabato, Tom Hess; 29th Annual Conference on Learning Theory, PMLR 49:1419-1439

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Best-of-K-bandits

Max Simchowitz, Kevin Jamieson, Benjamin Recht; 29th Annual Conference on Learning Theory, PMLR 49:1440-1489

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Memory, Communication, and Statistical Queries

Jacob Steinhardt, Gregory Valiant, Stefan Wager; 29th Annual Conference on Learning Theory, PMLR 49:1490-1516

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benefits of depth in neural networks

Matus Telgarsky; 29th Annual Conference on Learning Theory, PMLR 49:1517-1539

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A Guide to Learning Arithmetic Circuits

Ilya Volkovich; 29th Annual Conference on Learning Theory, PMLR 49:1540-1561

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Online learning in repeated auctions

Jonathan Weed, Vianney Perchet, Philippe Rigollet; 29th Annual Conference on Learning Theory, PMLR 49:1562-1583

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The Extended Littlestone’s Dimension for Learning with Mistakes and Abstentions

Chicheng Zhang, Kamalika Chaudhuri; 29th Annual Conference on Learning Theory, PMLR 49:1584-1616

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First-order Methods for Geodesically Convex Optimization

Hongyi Zhang, Suvrit Sra; 29th Annual Conference on Learning Theory, PMLR 49:1617-1638

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