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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
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
- 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
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 - Eric Blais, Ryan O'Donnell, Karl Wimmer:
Polynomial Regression under Arbitrary Product Distributions. 193-204
Generalization and Statistics
- 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
- 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 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
- 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
Generalization and Statistics
- 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
Open Problems
- Parikshit Gopalan, Adam Kalai, Adam R. Klivans:
A Query Algorithm for Agnostically Learning DNF?. 515-516
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