Preface

Sham M. Kakade, Ulrike von Luxburg; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:i-i

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Regret Bounds for the Adaptive Control of Linear Quadratic Systems

Yasin Abbasi-Yadkori, Csaba Szepesvári; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:1-26

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Blackwell Approachability and No-Regret Learning are Equivalent

Jacob Abernethy, Peter L. Bartlett, Elad Hazan; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:27-46

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Competitive Closeness Testing

Jayadev Acharya, Hirakendu Das, Ashkan Jafarpour, Alon Orlitsky, Shengjun Pan; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:47-68

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Oracle inequalities for computationally budgeted model selection

Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clement Levrard; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:69-86

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Bandits, Query Learning, and the Haystack Dimension

Kareem Amin, Michael Kearns, Umar Syed; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:87-106

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Minimax Policies for Combinatorial Prediction Games

Jean-Yves Audibert, Sébastien Bubeck, Gábor Lugosi; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:107-132

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Minimax Regret of Finite Partial-Monitoring Games in Stochastic Environments

Gábor Bartók, Dávid Pál, Csaba Szepesvári; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:133-154

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Sample Complexity Bounds for Differentially Private Learning

Kamalika Chaudhuri, Daniel Hsu; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:155-186

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Tight conditions for consistent variable selection in high dimensional nonparametric regression

Laëtitia Comminges, Arnak S. Dalalyan; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:187-206

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Multiclass Learnability and the ERM principle

Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:207-232

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Mixability is Bayes Risk Curvature Relative to Log Loss

Tim Erven, Mark D. Reid, Robert C. Williamson; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:233-252

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Distribution-Independent Evolvability of Linear Threshold Functions

Vitaly Feldman; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:253-272

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Lower Bounds and Hardness Amplification for Learning Shallow Monotone Formulas

Vitaly Feldman, Homin K. Lee, Rocco A. Servedio; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:273-292

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Complexity-Based Approach to Calibration with Checking Rules

Dean P. Foster, Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:293-314

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Concentration-Based Guarantees for Low-Rank Matrix Reconstruction

Rina Foygel, Nathan Srebro; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:315-340

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On the Consistency of Multi-Label Learning

Wei Gao, Zhi-Hua Zhou; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:341-358

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The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond

Aurélien Garivier, Olivier Cappé; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:359-376

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Sparsity Regret Bounds for Individual Sequences in Online Linear Regression

Sébastien Gerchinovitz; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:377-396

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

Peter Grünwald; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:397-420

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

Elad Hazan, Satyen Kale; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:421-436

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A Close Look to Margin Complexity and Related Parameters

Michael Kallweit, Hans Ulrich Simon; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:437-456

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

Wojciech Kotłowski, Peter Grünwald; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:457-476

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

Ping Li, Cun-Hui Zhang; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:477-496

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

Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:497-514

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Robust approachability and regret minimization in games with partial monitoring

Shie Mannor, Vianney Perchet, Gilles Stoltz; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:515-536

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The Rate of Convergence of Adaboost

Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:537-558

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Online Learning: Beyond Regret

Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:559-594

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Neyman-Pearson classification under a strict constraint

Philippe Rigollet, Xin Tong; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:595-614

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Sequential Event Prediction with Association Rules

Cynthia Rudin, Benjamin Letham, Ansaf Salleb-Aouissi, Eugene Kogan, David Madigan; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:615-634

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Optimal aggregation of affine estimators

Joseph Salmon, Arnak Dalalyan; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:635-660

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

Ohad Shamir, Shai Shalev-Shwartz; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:661-678

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Contextual Bandits with Similarity Information

Aleksandrs Slivkins; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:679-702

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Adaptive Density Level Set Clustering

Ingo Steinwart; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:703-738

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Agnostic KWIK learning and efficient approximate reinforcement learning

István Szita, Csaba Szepesvári; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:739-772

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The Sample Complexity of Dictionary Learning

Daniel Vainsencher, Shie Mannor, Alfred M. Bruckstein; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:773-788

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

Liu Yang, Steve Hanneke, Jaime Carbonell; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:789-806

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Does an Efficient Calibrated Forecasting Strategy Exist?

Jacob Abernethy, Shie Mannor; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:809-812

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Bounds on Individual Risk for Log-loss Predictors

Peter D. Grünwald, Wojciech Kotłowski; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:813-816

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

Elad Hazan, Satyen Kale; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:817-820

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Minimax Algorithm for Learning Rotations

Wojciech Kotłowski, Manfred K. Warmuth; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:821-824

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Missing Information Impediments to Learnability

Loizos Michael; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:825-828

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Monotone multi-armed bandit allocations

Aleksandrs Slivkins; Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:829-834

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