Table of Contents

• The Catch-Up Phenomenon in Bayesian InferencePeter Grunwald. 1-2 [doi]
• Combinatorial Prediction MarketsRobin Hanson. 3-4 [doi]
• Unsupervised Learning for Natural Language ProcessingDan Klein. 5-6 [doi]
• Concentration InequalitiesGábor Lugosi. 7-8 [doi]
• Learning Mixtures of Product Distributions Using Correlations and IndependenceKamalika Chaudhuri, Satish Rao. 9-20 [doi]
• Beyond Gaussians: Spectral Methods for Learning Mixtures of Heavy-Tailed DistributionsKamalika Chaudhuri, Satish Rao. 21-32 [doi]
• Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised LearningShai Ben-David, Tyler Lu, Dávid Pál. 33-44 [doi]
• The True Sample Complexity of Active LearningMaria-Florina Balcan, Steve Hanneke, Jennifer Wortman. 45-56 [doi]
• Extracting Certainty from Uncertainty: Regret Bounded by Variation in CostsElad Hazan, Satyen Kale. 57-68 [doi]
• Online Learning of Maximum p-Norm Margin Classifiers with BiasKosuke Ishibashi, Kohei Hatano, Masayuki Takeda. 69-80 [doi]
• Minimizing Wide Range Regret with Time Selection FunctionsSubhash Khot, Ashok Kumar Ponnuswami. 81-86 [doi]
• An Efficient Reduction of Ranking to ClassificationNir Ailon, Mehryar Mohri. 87-98 [doi]
• Learning from Collective BehaviorMichael Kearns, Jennifer Wortman. 99-110 [doi]
• Injective Hilbert Space Embeddings of Probability MeasuresBharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert R. G. Lanckriet, Bernhard Shoelkopf. 111-122 [doi]
• Almost Tight Upper Bound for Finding Fourier Coefficients of Bounded Pseudo- Boolean FunctionsSung-Soon Choi, Kyomin Jung, Jeong Han Kim. 123-134 [doi]
• Teaching Dimensions based on Cooperative LearningSandra Zilles, Steffen Lange, Robert Holte, Martin Zinkevich. 135-146 [doi]
• On the Power of Membership Queries in Agnostic LearningVitaly Feldman. 147-156 [doi]
• Dimension and Margin Bounds for Reflection-invariant KernelsThorsten Doliwa, Michael Kallweit, Hans-Ulrich Simon. 157-168 [doi]
• Learning Acyclic Probabilistic Circuits Using Test PathsDana Angluin, James Aspnes, Jiang Chen, David Eisenstat, Lev Reyzin. 169-180 [doi]
• Learning Random Monotone DNF Under the Uniform DistributionLinda Sellie. 181-192 [doi]
• Polynomial Regression under Arbitrary Product DistributionsEric Blais, Ryan O Donnell, Karl Wimmer. 193-204 [doi]
• How Local Should a Learning Method Be?Alon Zakai, Yaacov Ritov. 205-216 [doi]
• Learning Coordinate Gradients with Multi-Task KernelsYiming Ying, Colin Campbell. 217-228 [doi]
• Sparse Recovery in Large Ensembles of Kernel Machines On-Line Learning and BanditsVladimir Koltchinskii, Ming Yuan. 229-238 [doi]
• More Efficient Internal-Regret-Minimizing AlgorithmsAmy R. Greenwald, Zheng Li, Warren Schudy. 239-250 [doi]
• Linear Algorithms for Online Multitask ClassificationGiovanni Cavallanti, Nicolò Cesa-Bianchi, Claudio Gentile. 251-262 [doi]
• Competing in the Dark: An Efficient Algorithm for Bandit Linear OptimizationJacob Abernethy, Elad Hazan, Alexander Rakhlin. 263-274 [doi]
• Combining Expert Advice EfficientlyWouter M. Koolen, Steven de Rooij. 275-286 [doi]
• Improved Guarantees for Learning via Similarity FunctionsMaria-Florina Balcan, Avrim Blum, Nathan Srebro. 287-298 [doi]
• Geometric & Topological Representations of Maximum Classes with Applications to Sample CompressionJ. Hyam Rubinstein, Benjamin I. P. Rubinstein. 299-310 [doi]
• On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting AlgorithmsShai Shalev-Shwartz, Yoram Singer. 311-322 [doi]
• Adaptive Aggregation for Reinforcement Learning with Efficient Exploration: Deterministic DomainsAndrey Bernstein, Nahum Shimkin. 323-334 [doi]
• High-Probability Regret Bounds for Bandit Online Linear OptimizationPeter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham Kakade, Alexander Rakhlin, Ambuj Tewari. 335-342 [doi]
• Adapting to a Changing Environment: the Brownian Restless BanditsAleksandrs Slivkins, Eli Upfal. 343-354 [doi]
• Stochastic Linear Optimization under Bandit FeedbackVarsha Dani, Thomas P. Hayes, Sham M. Kakade. 355-366 [doi]
• Model Selection and Stability in k-means ClusteringOhad Shamir, Naftali Tishby. 367-378 [doi]
• Relating Clustering Stability to Properties of Cluster BoundariesShai Ben-David, Ulrike von Luxburg. 379-390 [doi]
• Finding Metric Structure in Information Theoretic ClusteringKamalika Chaudhuri, Andrew McGregor. 391-402 [doi]
• An Information Theoretic Framework for Multi-view LearningKarthik Sridharan, Sham M. Kakade. 403-414 [doi]
• Optimal Stragies and Minimax Lower Bounds for Online Convex GamesJacob Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari. 415-424 [doi]
• Regret Bounds for Sleeping Experts and BanditsRobert D. Kleinberg, Alexandru Niculescu-Mizil, Yogeshwer Sharma. 425-436 [doi]
• When Random Play is Optimal Against an AdversaryJacob Abernethy, Manfred K. Warmuth, Joel Yellin. 437-446 [doi]
• On-line Sequential Bin PackingAndrás György, Gábor Lugosi, György Ottucsák. 447-454 [doi]
• Time Varying Undirected GraphsShuheng Zhou, John D. Lafferty, Larry A. Wasserman. 455-466 [doi]
• Learning in the Limit with Adversarial DisturbancesConstantine Caramanis, Shie Mannor. 467-478 [doi]
• On the Margin Explanation of Boosting AlgorithmsLiwei Wang, Masashi Sugiyama, Cheng Yang, Zhi-Hua Zhou, Jufu Feng. 479-490 [doi]
• Adaptive Hausdorff Estimation of Density Level SetsAarti Singh, Robert Nowak, Clayton Scott. 491-502 [doi]
• Density Estimation in Linear TimeSatyaki Mahalanabis, Daniel Stefankovic. 503-512 [doi]
• The Learning Power of EvolutionVitaly Feldman, Leslie G. Valiant. 513-514 [doi]
• A Query Algorithm for Agnostically Learning DNF?Parikshit Gopalan, Adam Kalai, Adam R. Klivans. 515-516 [doi]
• Learning RotationsAdam M. Smith, Manfred K. Warmuth. 517 [doi]