Volume 19: Proceedings of the 24th Annual Conference on Learning Theory, 9-11 June 2011, Budapest, Hungary

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Editors: Sham M. Kakade, Ulrike von Luxburg

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Contents:

Preface

Preface

Sham M. Kakade, Ulrike von Luxburg ; PMLR 19:i-i

Accepted Papers

Regret Bounds for the Adaptive Control of Linear Quadratic Systems

Yasin Abbasi-Yadkori, Csaba Szepesvári ; PMLR 19:1-26

Blackwell Approachability and No-Regret Learning are Equivalent

Jacob Abernethy, Peter L. Bartlett, Elad Hazan ; PMLR 19:27-46

Competitive Closeness Testing

Jayadev Acharya, Hirakendu Das, Ashkan Jafarpour, Alon Orlitsky, Shengjun Pan ; PMLR 19:47-68

Oracle inequalities for computationally budgeted model selection

Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clement Levrard ; PMLR 19:69-86

Bandits, Query Learning, and the Haystack Dimension

Kareem Amin, Michael Kearns, Umar Syed ; PMLR 19:87-106

Minimax Policies for Combinatorial Prediction Games

Jean-Yves Audibert, Sébastien Bubeck, Gábor Lugosi ; PMLR 19:107-132

Minimax Regret of Finite Partial-Monitoring Games in Stochastic Environments

Gábor Bartók, Dávid Pál, Csaba Szepesvári ; PMLR 19:133-154

Sample Complexity Bounds for Differentially Private Learning

Kamalika Chaudhuri, Daniel Hsu ; PMLR 19:155-186

Tight conditions for consistent variable selection in high dimensional nonparametric regression

Laëtitia Comminges, Arnak S. Dalalyan ; PMLR 19:187-206

Multiclass Learnability and the ERM principle

Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz ; PMLR 19:207-232

Mixability is Bayes Risk Curvature Relative to Log Loss

Tim Erven, Mark D. Reid, Robert C. Williamson ; PMLR 19:233-252

Distribution-Independent Evolvability of Linear Threshold Functions

Vitaly Feldman ; PMLR 19:253-272

Lower Bounds and Hardness Amplification for Learning Shallow Monotone Formulas

Vitaly Feldman, Homin K. Lee, Rocco A. Servedio ; PMLR 19:273-292

Complexity-Based Approach to Calibration with Checking Rules

Dean P. Foster, Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari ; PMLR 19:293-314

Concentration-Based Guarantees for Low-Rank Matrix Reconstruction

Rina Foygel, Nathan Srebro ; PMLR 19:315-340

On the Consistency of Multi-Label Learning

Wei Gao, Zhi-Hua Zhou ; PMLR 19:341-358

The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond

Aurélien Garivier, Olivier Cappé ; PMLR 19:359-376

Sparsity Regret Bounds for Individual Sequences in Online Linear Regression

Sébastien Gerchinovitz ; PMLR 19:377-396

Safe Learning: bridging the gap between Bayes, MDL and statistical learning theory via empirical convexity

Peter Grünwald ; PMLR 19:397-420

Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization

Elad Hazan, Satyen Kale ; PMLR 19:421-436

A Close Look to Margin Complexity and Related Parameters

Michael Kallweit, Hans Ulrich Simon ; PMLR 19:437-456

Maximum Likelihood vs. Sequential Normalized Maximum Likelihood in On-line Density Estimation

Wojciech Kotłowski, Peter Grünwald ; PMLR 19:457-476

A New Algorithm for Compressed Counting with Applications in Shannon Entropy Estimation in Dynamic Data

Ping Li, Cun-Hui Zhang ; PMLR 19:477-496

A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences

Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz ; PMLR 19:497-514

Robust approachability and regret minimization in games with partial monitoring

Shie Mannor, Vianney Perchet, Gilles Stoltz ; PMLR 19:515-536

The Rate of Convergence of Adaboost

Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire ; PMLR 19:537-558

Online Learning: Beyond Regret

Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari ; PMLR 19:559-594

Neyman-Pearson classification under a strict constraint

Philippe Rigollet, Xin Tong ; PMLR 19:595-614

Sequential Event Prediction with Association Rules

Cynthia Rudin, Benjamin Letham, Ansaf Salleb-Aouissi, Eugene Kogan, David Madigan ; PMLR 19:615-634

Optimal aggregation of affine estimators

Joseph Salmon, Arnak Dalalyan ; PMLR 19:635-660

Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing

Ohad Shamir, Shai Shalev-Shwartz ; PMLR 19:661-678

Contextual Bandits with Similarity Information

Aleksandrs Slivkins ; PMLR 19:679-702

Adaptive Density Level Set Clustering

Ingo Steinwart ; PMLR 19:703-738

Agnostic KWIK learning and efficient approximate reinforcement learning

István Szita, Csaba Szepesvári ; PMLR 19:739-772

The Sample Complexity of Dictionary Learning

Daniel Vainsencher, Shie Mannor, Alfred M. Bruckstein ; PMLR 19:773-788

Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning

Liu Yang, Steve Hanneke, Jaime Carbonell ; PMLR 19:789-806

Does an Efficient Calibrated Forecasting Strategy Exist?

Jacob Abernethy, Shie Mannor ; PMLR 19:809-812

Bounds on Individual Risk for Log-loss Predictors

Peter D. Grünwald, Wojciech Kotłowski ; PMLR 19:813-816

A simple multi-armed bandit algorithm with optimal variation-bounded regret

Elad Hazan, Satyen Kale ; PMLR 19:817-820

Minimax Algorithm for Learning Rotations

Wojciech Kotłowski, Manfred K. Warmuth ; PMLR 19:821-824

Missing Information Impediments to Learnability

Loizos Michael ; PMLR 19:825-828

Monotone multi-armed bandit allocations

Aleksandrs Slivkins ; PMLR 19:829-834

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