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Semantic Scholar21. COLT 2008: Helsinki, Finland
Rocco A. Servedio, Tong Zhang:
21st Annual Conference on Learning Theory - COLT 2008, Helsinki, Finland, July 9-12, 2008. Omnipress 2008
Invited Presentations
Peter Grunwald:
The Catch-Up Phenomenon in Bayesian Inference. 1-2Robin Hanson:
Combinatorial Prediction Markets. 3-4Dan Klein:
Unsupervised Learning for Natural Language Processing. 5-6Gábor Lugosi:
Concentration Inequalities. 7-8
Unsupervised, Semi-Supervised and Active Learning
Kamalika Chaudhuri, Satish Rao:
Learning Mixtures of Product Distributions Using Correlations and Independence. 9-20Kamalika Chaudhuri, Satish Rao:
Beyond Gaussians: Spectral Methods for Learning Mixtures of Heavy-Tailed Distributions. 21-32Shai Ben-David, Tyler Lu, Dávid Pál:
Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning. 33-44Maria-Florina Balcan, Steve Hanneke, Jennifer Wortman:
The True Sample Complexity of Active Learning. 45-56
On-Line Learning
Elad Hazan, Satyen Kale:
Extracting Certainty from Uncertainty: Regret Bounded by Variation in Costs. 57-68Kosuke Ishibashi, Kohei Hatano, Masayuki Takeda:
Online Learning of Maximum p-Norm Margin Classifiers with Bias. 69-80Subhash Khot, Ashok Kumar Ponnuswami:
Minimizing Wide Range Regret with Time Selection Functions. 81-86
Other Directions
Nir Ailon, Mehryar Mohri:
An Efficient Reduction of Ranking to Classification. 87-98Michael J. Kearns, Jennifer Wortman:
Learning from Collective Behavior. 99-110Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert R. G. Lanckriet, Bernhard Schölkopf:
Injective Hilbert Space Embeddings of Probability Measures. 111-122
Complexity and Boolean Functions
Sung-Soon Choi, Kyomin Jung, Jeong Han Kim:
Almost Tight Upper Bound for Finding Fourier Coefficients of Bounded Pseudo- Boolean Functions. 123-134Sandra Zilles, Steffen Lange, Robert Holte, Martin Zinkevich:
Teaching Dimensions based on Cooperative Learning. 135-146Vitaly Feldman:
On the Power of Membership Queries in Agnostic Learning. 147-156
Complexity and Boolean Functions
Thorsten Doliwa, Michael Kallweit, Hans Ulrich Simon:
Dimension and Margin Bounds for Reflection-invariant Kernels. 157-168Dana Angluin, James Aspnes, Jiang Chen, David Eisenstat, Lev Reyzin:
Learning Acyclic Probabilistic Circuits Using Test Paths. 169-180Linda Sellie:
Learning Random Monotone DNF Under the Uniform Distribution. 181-192Eric Blais, Ryan O'Donnell, Karl Wimmer:
Polynomial Regression under Arbitrary Product Distributions. 193-204
Generalization and Statistics
Alon Zakai, Yaacov Ritov:
How Local Should a Learning Method Be?. 205-216Yiming Ying, Colin Campbell:
Learning Coordinate Gradients with Multi-Task Kernels. 217-228Vladimir Koltchinskii, Ming Yuan:
Sparse Recovery in Large Ensembles of Kernel Machines On-Line Learning and Bandits. 229-238Amy Greenwald, Zheng Li, Warren Schudy:
More Efficient Internal-Regret-Minimizing Algorithms. 239-250Giovanni Cavallanti, Nicolò Cesa-Bianchi, Claudio Gentile:
Linear Algorithms for Online Multitask Classification. 251-262Jacob D. Abernethy, Elad Hazan, Alexander Rakhlin:
Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization. 263-274
Other Directions
Wouter M. Koolen, Steven de Rooij:
Combining Expert Advice Efficiently. 275-286Maria-Florina Balcan, Avrim Blum, Nathan Srebro:
Improved Guarantees for Learning via Similarity Functions. 287-298J. Hyam Rubinstein, Benjamin I. P. Rubinstein:
Geometric & Topological Representations of Maximum Classes with Applications to Sample Compression. 299-310Shai Shalev-Shwartz, Yoram Singer:
On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms. 311-322
Bandits and Reinforcement Learning
Andrey Bernstein, Nahum Shimkin:
Adaptive Aggregation for Reinforcement Learning with Efficient Exploration: Deterministic Domains. 323-334Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham M. Kakade, Alexander Rakhlin, Ambuj Tewari:
High-Probability Regret Bounds for Bandit Online Linear Optimization. 335-342Aleksandrs Slivkins, Eli Upfal:
Adapting to a Changing Environment: the Brownian Restless Bandits. 343-354Varsha Dani, Thomas P. Hayes, Sham M. Kakade:
Stochastic Linear Optimization under Bandit Feedback. 355-366
Unsupervised and Semi-Supervised Learning
Ohad Shamir, Naftali Tishby:
Model Selection and Stability in k-means Clustering. 367-378Shai Ben-David, Ulrike von Luxburg:
Relating Clustering Stability to Properties of Cluster Boundaries. 379-390Kamalika Chaudhuri, Andrew McGregor:
Finding Metric Structure in Information Theoretic Clustering. 391-402Karthik Sridharan, Sham M. Kakade:
An Information Theoretic Framework for Multi-view Learning. 403-414
Online Learning
Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari:
Optimal Stragies and Minimax Lower Bounds for Online Convex Games. 415-424Robert D. Kleinberg, Alexandru Niculescu-Mizil, Yogeshwer Sharma:
Regret Bounds for Sleeping Experts and Bandits. 425-436Jacob D. Abernethy, Manfred K. Warmuth, Joel Yellin:
When Random Play is Optimal Against an Adversary. 437-446András György, Gábor Lugosi, György Ottucsák:
On-line Sequential Bin Packing. 447-454
Generalization and Statistics
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Time Varying Undirected Graphs. 455-466Constantine Caramanis, Shie Mannor:
Learning in the Limit with Adversarial Disturbances. 467-478Liwei Wang, Masashi Sugiyama, Cheng Yang, Zhi-Hua Zhou, Jufu Feng:
On the Margin Explanation of Boosting Algorithms. 479-490Aarti Singh, Robert D. Nowak, Clayton D. Scott:
Adaptive Hausdorff Estimation of Density Level Sets. 491-502Satyaki Mahalanabis, Daniel Stefankovic:
Density Estimation in Linear Time. 503-512
Open Problems
Vitaly Feldman, Leslie G. Valiant:
The Learning Power of Evolution. 513-514Parikshit Gopalan, Adam Kalai, Adam R. Klivans:
A Query Algorithm for Agnostically Learning DNF?. 515-516Adam M. Smith, Manfred K. Warmuth:
Learning Rotations. 517
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