


default search action
21st 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. 1-2 - Robin Hanson:

Combinatorial Prediction Markets. 3-4 - Dan Klein:

Unsupervised Learning for Natural Language Processing. 5-6 - Gábor Lugosi:

Concentration Inequalities. 7-8
Unsupervised, Semi-Supervised and Active Learning
- Kamalika Chaudhuri, Satish Rao:

Learning Mixtures of Product Distributions Using Correlations and Independence. 9-20 - Kamalika Chaudhuri, Satish Rao:

Beyond Gaussians: Spectral Methods for Learning Mixtures of Heavy-Tailed Distributions. 21-32 - Shai Ben-David, Tyler Lu, Dávid Pál:

Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning. 33-44 - Maria-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. 57-68 - Kosuke Ishibashi, Kohei Hatano, Masayuki Takeda:

Online Learning of Maximum p-Norm Margin Classifiers with Bias. 69-80 - Subhash 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. 87-98 - Michael J. Kearns, Jennifer Wortman:

Learning from Collective Behavior. 99-110 - Bharath 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. 123-134 - Sandra Zilles, Steffen Lange, Robert Holte, Martin Zinkevich:

Teaching Dimensions based on Cooperative Learning. 135-146 - Vitaly 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. 157-168 - Dana Angluin, James Aspnes, Jiang Chen, David Eisenstat, Lev Reyzin:

Learning Acyclic Probabilistic Circuits Using Test Paths. 169-180 - Linda Sellie:

Learning Random Monotone DNF Under the Uniform Distribution. 181-192 - Eric 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?. 205-216 - Yiming Ying, Colin Campbell:

Learning Coordinate Gradients with Multi-Task Kernels. 217-228 - Vladimir Koltchinskii, Ming Yuan:

Sparse Recovery in Large Ensembles of Kernel Machines On-Line Learning and Bandits. 229-238 - Amy Greenwald, Zheng Li, Warren Schudy:

More Efficient Internal-Regret-Minimizing Algorithms. 239-250 - Giovanni Cavallanti, Nicolò Cesa-Bianchi, Claudio Gentile:

Linear Algorithms for Online Multitask Classification. 251-262 - Jacob 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. 275-286 - Maria-Florina Balcan, Avrim Blum, Nathan Srebro:

Improved Guarantees for Learning via Similarity Functions. 287-298 - J. Hyam Rubinstein, Benjamin I. P. Rubinstein:

Geometric & Topological Representations of Maximum Classes with Applications to Sample Compression. 299-310 - Shai 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. 323-334 - Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham M. Kakade, Alexander Rakhlin, Ambuj Tewari:

High-Probability Regret Bounds for Bandit Online Linear Optimization. 335-342 - Aleksandrs Slivkins, Eli Upfal:

Adapting to a Changing Environment: the Brownian Restless Bandits. 343-354 - Varsha 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. 367-378 - Shai Ben-David, Ulrike von Luxburg:

Relating Clustering Stability to Properties of Cluster Boundaries. 379-390 - Kamalika Chaudhuri, Andrew McGregor:

Finding Metric Structure in Information Theoretic Clustering. 391-402 - Karthik 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. 415-424 - Robert D. Kleinberg, Alexandru Niculescu-Mizil, Yogeshwer Sharma:

Regret Bounds for Sleeping Experts and Bandits. 425-436 - Jacob D. Abernethy, Manfred K. Warmuth, Joel Yellin:

When Random Play is Optimal Against an Adversary. 437-446 - András 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. 455-466 - Constantine Caramanis, Shie Mannor:

Learning in the Limit with Adversarial Disturbances. 467-478 - Liwei Wang, Masashi Sugiyama, Cheng Yang, Zhi-Hua Zhou, Jufu Feng:

On the Margin Explanation of Boosting Algorithms. 479-490 - Aarti Singh, Robert D. Nowak, Clayton D. Scott:

Adaptive Hausdorff Estimation of Density Level Sets. 491-502 - Satyaki Mahalanabis, Daniel Stefankovic:

Density Estimation in Linear Time. 503-512
Open Problems
- Vitaly Feldman, Leslie G. Valiant:

The Learning Power of Evolution. 513-514 - Parikshit Gopalan, Adam Kalai, Adam R. Klivans:

A Query Algorithm for Agnostically Learning DNF?. 515-516 - Adam M. Smith, Manfred K. Warmuth:

Learning Rotations. 517

manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.


Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID













