Volume 19, Dezember 2011

• 
  Sham M. Kakade, Ulrike von Luxburg: Preface.
• 
  Yasin Abbasi-Yadkori, Csaba Szepesvári: Regret Bounds for the Adaptive Control of Linear Quadratic Systems. 1-26
• 
  Jacob 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, Clement Levrard: Oracle inequalities for computationally budgeted model selection. 69-86
• 
  Kareem Amin, Michael 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 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, 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

• 
  Jacob 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

last updated on Wed May 22 05:55:38 2013 by the dblp team
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