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ORCID24. COLT 2011: Budapest, Hungary
- Sham M. Kakade, Ulrike von Luxburg:
COLT 2011 - The 24th Annual Conference on Learning Theory, June 9-11, 2011, Budapest, Hungary. JMLR Proceedings 19, JMLR.org 2011 - Sham M. Kakade, Ulrike von Luxburg:
Preface. COLT 2011 - Yasin Abbasi-Yadkori, Csaba Szepesvári:
Regret Bounds for the Adaptive Control of Linear Quadratic Systems. 1-26 - Jacob D. Abernethy, Peter L. Bartlett, Elad Hazan:
Blackwell Approachability and No-Regret Learning are Equivalent. 27-46 - Jayadev Acharya, Hirakendu Das, Ashkan Jafarpour, Alon Orlitsky, Shengjun Pan:
Competitive Closeness Testing. 47-68 - Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clément Levrard:
Oracle inequalities for computationally budgeted model selection. 69-86 - Kareem Amin, Michael J. Kearns, Umar Syed:
Bandits, Query Learning, and the Haystack Dimension. 87-106 - Jean-Yves Audibert, Sébastien Bubeck, Gábor Lugosi:
Minimax Policies for Combinatorial Prediction Games. 107-132 - Gábor Bartók, Dávid Pál, Csaba Szepesvári:
Minimax Regret of Finite Partial-Monitoring Games in Stochastic Environments. 133-154 - Kamalika Chaudhuri, Daniel J. Hsu:
Sample Complexity Bounds for Differentially Private Learning. 155-186 - Laëtitia Comminges, Arnak S. Dalalyan:
Tight conditions for consistent variable selection in high dimensional nonparametric regression. 187-206 - Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz:
Multiclass Learnability and the ERM principle. 207-232 - Tim van Erven, Mark D. Reid, Robert C. Williamson:
Mixability is Bayes Risk Curvature Relative to Log Loss. 233-252 - Vitaly Feldman:
Distribution-Independent Evolvability of Linear Threshold Functions. 253-272 - Vitaly Feldman, Homin K. Lee, Rocco A. Servedio:
Lower Bounds and Hardness Amplification for Learning Shallow Monotone Formulas. 273-292 - Dean P. Foster, Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Complexity-Based Approach to Calibration with Checking Rules. 293-314 - Rina Foygel, Nathan Srebro:
Concentration-Based Guarantees for Low-Rank Matrix Reconstruction. 315-340 - Wei Gao, Zhi-Hua Zhou:
On the Consistency of Multi-Label Learning. 341-358 - Aurélien Garivier, Olivier Cappé:
The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond. 359-376 - Sébastien Gerchinovitz:
Sparsity Regret Bounds for Individual Sequences in Online Linear Regression. 377-396 - Peter Grünwald, John Smith Jones, Jane de Winter, Élouise Smith:
Safe Learning: bridging the gap between Bayes, MDL and statistical learning theory via empirical convexity. 397-420 - Elad Hazan, Satyen Kale:
Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization. 421-436 - Michael Kallweit, Hans Ulrich Simon:
A Close Look to Margin Complexity and Related Parameters. 437-456 - Wojciech Kotlowski, Peter Grünwald:
Maximum Likelihood vs. Sequential Normalized Maximum Likelihood in On-line Density Estimation. 457-476 - Ping Li, Cun-Hui Zhang:
A New Algorithm for Compressed Counting with Applications in Shannon Entropy Estimation in Dynamic Data. 477-496 - Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz:
A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences. 497-514 - Shie Mannor, Vianney Perchet, Gilles Stoltz:
Robust approachability and regret minimization in games with partial monitoring. 515-536 - Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire:
The Rate of Convergence of Adaboost. 537-558 - Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Beyond Regret. 559-594 - Philippe Rigollet, Xin Tong:
Neyman-Pearson classification under a strict constraint. 595-614 - Cynthia Rudin, Benjamin Letham, Ansaf Salleb-Aouissi, Eugene Kogan, David Madigan:
Sequential Event Prediction with Association Rules. 615-634 - Joseph Salmon, Arnak S. Dalalyan:
Optimal aggregation of affine estimators. 635-660 - Ohad Shamir, Shai Shalev-Shwartz:
Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing. 661-678 - Aleksandrs Slivkins:
Contextual Bandits with Similarity Information. 679-702 - Ingo Steinwart:
Adaptive Density Level Set Clustering. 703-738 - István Szita, Csaba Szepesvári:
Agnostic KWIK learning and efficient approximate reinforcement learning. 739-772 - Daniel Vainsencher, Shie Mannor, Alfred M. Bruckstein:
The Sample Complexity of Dictionary Learning. 773-788 - Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning. 789-806
Open problems
- Jacob D. Abernethy, Shie Mannor:
Does an Efficient Calibrated Forecasting Strategy Exist? 809-812 - Peter D. Grünwald, Wojciech Kotlowski:
Bounds on Individual Risk for Log-loss Predictors. 813-816 - Elad Hazan, Satyen Kale:
A simple multi-armed bandit algorithm with optimal variation-bounded regret. 817-820 - Wojciech Kotlowski, Manfred K. Warmuth:
Minimax Algorithm for Learning Rotations. 821-824 - Loizos Michael:
Missing Information Impediments to Learnability. 825-828 - Aleksandrs Slivkins:
Monotone multi-armed bandit allocations. 829-834
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