Accepted papers

• Kamalika Chaudhuri and Satish Rao. Learning Mixtures of Product Distributions Using Correlations and Independence
• Liwei Wang, Masashi Sugiyama, Cheng Yang, Zhi-Hua Zhou and Jufu Feng. On the Margin Explanation of Boosting Algorithms
• Andrey Bernstein and Nahum Shimkin. Adaptive Aggregation for Reinforcement Learning with Efficient Exploration: Deterministic Domains
• Alon Zakai and Yaacov Ritov. How Local Should a Learning Method Be?
• Yiming Ying and Colin Campbell. Learning coordinate gradients with multi-task kernels
• Shai Shalev-Shwartz and Yoram Singer. On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms
• Peter Bartlett, Varsha Dani, Thomas Hayes, Sham Kakade, Alexander Rakhlin and Ambuj Tewari. High-Probability Regret Bounds for Bandit Online Linear Optimization
• Vladimir Koltchinskii and Ming Yuan. Sparse Recovery in Large Ensembles of Kernel Machines
• Nir Ailon and Mehryar Mohri. An Efficient Reduction of Ranking to Classification
• Aarti Singh and Robert Nowack and Clayton Scott. Adaptive Hausdorff Estimation of Density Level Sets
• Kamalika Chaudhuri and Satish Rao. Beyond Gaussians: Spectral Methods for Learning Mixtures of Heavy-Tailed Distributions
• Aleksandrs Slivkins and Eli Upfal. Adapting to a Changing Environment: the Brownian Restless Bandits
• Elad Hazan and Satyen Kale. Extracting Certainty from Uncertainty: Regret Bounded by Variation in Costs
• Kosuke Ishibashi, Kohei Hatano and Masayuki Takeda. Online Learning of Maximum p-Norm Margin Classifiers with Bias
• Andras Gyorgy, Gabor Lugosi and Gyorgy Ottucsak. On-line sequential bin packing
• Amy Greenwald, Zheng Li and Warren Schudy. More Efficient Internal-Regret-Minimizing Algorithms
• Michael Kearns and Jennifer Wortman. Learning from Collective Behavior
• Thorsten Doliwa, Michael Kallweit and Hans Ulrich Simon. Dimension and Margin Bounds for Reflection-invariant Kernels
• Dana Angluin, James Aspnes, Jiang Chen, David Eisenstat and Lev Reyzin. Learning Acyclic Probabilistic Circuits Using Test Paths
• Linda Sellie. Learning Random Monotone DNF Under the Uniform Distribution
• Eric Blais, Ryan O'Donnell and Karl Wimmer. Polynomial regression under arbitrary product distributions
• Varsha Dani, Thomas P. Hayes and Sham M. Kakade. Stochastic Linear Optimization under Bandit Feedback
• Shuheng Zhou, John Lafferty and Larry Wasserman. Time Varying Undirected Graphs
• Wouter M. Koolen and Steven De Rooij. Combining Expert Advice Efficiently
• Subhash Khot and Ashok Kumar Ponnuswami. Minimizing Wide Range Regret with Time Selection Functions
• Maria-Florina Balcan, Avrim Blum and Nathan Srebo. Improved Guarantees for Learning via Similarity Functions
• Benjamin I. P. Rubinstein and J. Hyam Rubinstein. Geometric & Topological Representations of Maximum Classes with Applications to Sample Compression
• Shai Ben-David and Ulrike von Luxburg. Relating clustering stability to properties of cluster boundaries
• Shai Ben-David, Tyler Lu and David Pal. Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning
• Karthik Sridharan and Sham M. Kakade. An Information Theoretic Framework for Multi-view Learning
• Bharath Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert Lanckriet and Bernhard Shoelkopf. Injective Hilbert Space Embeddings of Probability Measures
• Kamalika Chaudhuri and Andrew McGregor. Finding Metric Structure in Information Theoretic Clustering
• Satyaki Mahalanabis and Daniel Stefankovic. Density estimation in linear time
• Constantine Caramanis and Shie Mannor. Learning in the Limit with Adversarial Disturbances
• Giovanni Cavallanti, Nicolo' Cesa-Bianchi and Claudio Gentile. Linear Algorithms for Online Multitask Classification
• Maria-Florina Balcan, Steve Hanneke and Jennifer Wortman. The True Sample Complexity of Active Learning
• Jacob Abernethy, Peter Bartlett, Alexander Rakhlin and Ambuj Tewari. Optimal Stragies and Minimax Lower Bounds for Online Convex Games
• Robert D. Kleinberg, Alexandru Niculescu-Mizil and Yogeshwer Sharma. Regret Bounds for Sleeping Experts and Bandits
• Jacob Abernethy, Manfred K. Warmuth and Joel Yellin. When random play is optimal against an adversary
• Jacob Abernethy, Elad Hazan and Alexander Rakhlin. Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization
• Sung-Soon Choi, Kyomin Jung and Jeong Han Kim. Almost Tight Upper Bound for Finding Fourier Coefficients of Bounded Pseudo-Boolean Functions
• Sandra Zilles, Steffen Lange, Robert Holte, and Martin Zinkevich. Teaching Dimensions based on Cooperative Learning
• Vitaly Feldman. On the Power of Membership Queries in Agnostic Learning
• Ohad Shamir and Naftali Tishby. Model Selection and Stability in k-means Clustering