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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 30: Conference on Learning Theory, 12-14 June 2013, Princeton, NJ, USA

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Editors: Shai Shalev-Shwartz, Ingo Steinwart

[bib][citeproc]

Contents:

  • Preface
  • Regular Papers
  • Open Problems

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Preface

Preface

; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:1-2

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Regular Papers

Open Problems

Open Problem: Adversarial Multiarmed Bandits with Limited Advice

Yevgeny Seldin, Koby Crammer, Peter Bartlett; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:1067-1072

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Open Problem: Fast Stochastic Exp-Concave Optimization

Tomer Koren; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:1073-1075

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Open Problem: Lower bounds for Boosting with Hadamard Matrices

Jiazhong Nie, Manfred K. Warmuth, S.V.N. Vishwanathan, Xinhua Zhang; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:1076-1079

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On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization

Ohad Shamir; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:3-24

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A Theoretical Analysis of NDCG Type Ranking Measures

Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Tie-Yan Liu; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:25-54

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Excess risk bounds for multitask learning with trace norm regularization

Massimiliano Pontil, Andreas Maurer; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:55-76

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Honest Compressions and Their Application to Compression Schemes

Roi Livni, Pierre Simon; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:77-92

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The price of bandit information in multiclass online classification

Amit Daniely, Tom Helbertal; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:93-104

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Estimation of Extreme Values and Associated Level Sets of a Regression Function via Selective Sampling

Stanislav Minsker; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:105-121

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Bounded regret in stochastic multi-armed bandits

Sébastien Bubeck, Vianney Perchet, Philippe Rigollet; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:122-134

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Recovering the Optimal Solution by Dual Random Projection

Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:135-157

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Opportunistic Strategies for Generalized No-Regret Problems

Andrey Bernstein, Shie Mannor, Nahum Shimkin; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:158-171

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Online Learning for Time Series Prediction

Oren Anava, Elad Hazan, Shie Mannor, Ohad Shamir; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:172-184

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Sharp analysis of low-rank kernel matrix approximations

Francis Bach; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:185-209

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Beating Bandits in Gradually Evolving Worlds

Chao-Kai Chiang, Chia-Jung Lee, Chi-Jen Lu; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:210-227

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Information Complexity in Bandit Subset Selection

Emilie Kaufmann, Shivaram Kalyanakrishnan; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:228-251

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Passive Learning with Target Risk

Mehrdad Mahdavi, Rong Jin; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:252-269

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Blind Signal Separation in the Presence of Gaussian Noise

Mikhail Belkin, Luis Rademacher, James Voss; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:270-287

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Active and passive learning of linear separators under log-concave distributions

Maria-Florina Balcan, Phil Long; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:288-316

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Randomized partition trees for exact nearest neighbor search

Sanjoy Dasgupta, Kaushik Sinha; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:317-337

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Surrogate Regret Bounds for the Area Under the ROC Curve via Strongly Proper Losses

Shivani Agarwal; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:338-353

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Algorithms and Hardness for Robust Subspace Recovery

Moritz Hardt, Ankur Moitra; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:354-375

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PLAL: Cluster-based active learning

Ruth Urner, Sharon Wulff, Shai Ben-David; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:376-397

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Learning Using Local Membership Queries

Pranjal Awasthi, Vitaly Feldman, Varun Kanade; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:398-431

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Sparse Adaptive Dirichlet-Multinomial-like Processes

Marcus Hutter; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:432-459

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Prediction by random-walk perturbation

Luc Devroye, Gábor Lugosi, Gergely Neu; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:460-473

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Approachability, fast and slow

Vianney Perchet, Shie Mannor; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:474-488

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Classification with Asymmetric Label Noise: Consistency and Maximal Denoising

Clayton Scott, Gilles Blanchard, Gregory Handy; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:489-511

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General Oracle Inequalities for Gibbs Posterior with Application to Ranking

Cheng Li, Wenxin Jiang, Martin Tanner; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:512-521

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Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching

Daniel Kane, Adam Klivans, Raghu Meka; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:522-545

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Subspace Embeddings and \ell_p-Regression Using Exponential Random Variables

David Woodruff, Qin Zhang; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:546-567

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Consistency of Robust Kernel Density Estimators

Robert Vandermeulen, Clayton Scott; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:568-591

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Divide and Conquer Kernel Ridge Regression

Yuchen Zhang, John Duchi, Martin Wainwright; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:592-617

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Regret Minimization for Branching Experts

Eyal Gofer, Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:618-638

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Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families

Peter Bartlett, Peter Grünwald, Peter Harremoës, Fares Hedayati, Wojciech Kotlowski; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:639-661

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Online Similarity Prediction of Networked Data from Known and Unknown Graphs

Claudio Gentile, Mark Herbster, Stephen Pasteris; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:662-695

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A near-optimal algorithm for finite partial-monitoring games against adversarial opponents

Gábor Bartók; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:696-710

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Representation, Approximation and Learning of Submodular Functions Using Low-rank Decision Trees

Vitaly Feldman, Pravesh Kothari, Jan Vondrák; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:711-740

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A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and Regret

Lachlan Andrew, Siddharth Barman, Katrina Ligett, Minghong Lin, Adam Meyerson, Alan Roytman, Adam Wierman; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:741-763

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Optimal Probability Estimation with Applications to Prediction and Classification

Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:764-796

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Polynomial Time Optimal Query Algorithms for Finding Graphs with Arbitrary Real Weights

Sung-Soon Choi; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:797-818

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Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso

Abhradeep Guha Thakurta, Adam Smith; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:819-850

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Learning a set of directions

Wouter M. Koolen, Jiazhong Nie, Manfred Warmuth; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:851-866

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A Tensor Spectral Approach to Learning Mixed Membership Community Models

Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham Kakade; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:867-881

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Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem

Ittai Abraham, Omar Alonso, Vasilis Kandylas, Aleksandrs Slivkins; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:882-910

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Boosting with the Logistic Loss is Consistent

Matus Telgarsky; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:911-965

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Competing With Strategies

Wei Han, Alexander Rakhlin, Karthik Sridharan; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:966-992

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Online Learning with Predictable Sequences

Alexander Rakhlin, Karthik Sridharan; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:993-1019

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Efficient Learning of Simplices

Joseph Anderson, Navin Goyal, Luis Rademacher; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:1020-1045

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Complexity Theoretic Lower Bounds for Sparse Principal Component Detection

Quentin Berthet, Philippe Rigollet; Proceedings of the 26th Annual Conference on Learning Theory, PMLR 30:1046-1066

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