Overview of the ICML, UAI, COLT and MLG 2008 Programme
| ICML 2008 | UAI 2008 | COLT 2008 | MLG 2008 | |||||||
| Fri | 4 July | MLG Technical sessions | ||||||||
| 17:00-19:00 MLG Poster session | ||||||||||
| 20:00-23:00 MLG Banquet | ||||||||||
| Sat | 5 July | 9:00-18:30 ICML Tutorials | MLG Technical sessions | |||||||
| Sun | 6 July | 8:30-17:45 ICML Technical sessions | ||||||||
| 18:00-20:30 ICML Poster session I | ||||||||||
| Mon | 7 July | 8:30-17:00 ICML Technical sessions | ||||||||
| 18:30-22:30 ICML Banquet | ||||||||||
| Tue | 8 July | 8:30-17:30 ICML Technical sessions | ||||||||
| 18:00-20:30 ICML Poster session II | ||||||||||
| Wed | July | 9:00-18:00 ICML/UAI/COLT joint workshop day | ||||||||
| 18:00-20:00 ICML/UAI/COLT joint reception in the Main Building | ||||||||||
| Thu | 10 July | 9:00-16:50 UAI Technical sessions | 8:30-18:30 COLT Technical sessions | |||||||
| 17:15-18:15 UAI Poster Spotlights | 18:30-19:00 COLT Open Problem session | |||||||||
| 18:15-21:00 UAI Poster session | 20:00-21:30 COLT Business meeting | |||||||||
| Fri | 11 July | 9:00-18:30 UAI Technical sessions | 8:30-19:00 COLT Technical sessions | |||||||
| 18:30-20:00 UAI Business meeting | 19:00-20:00 COLT Rump session | |||||||||
| 20:00-23:00 Joint UAI/COLT Banquet | ||||||||||
| Sat | 12 July | 9:00-15:30 UAI Technical sessions | 8:30-18:00 COLT Technical sessions | |||||||
COLT 2008 Schedule
Thursday, July 10
8:30-8:35: Opening remarks
8:35-10:15 Unsupervised, Semi-Supervised and Active Learning
- Kamalika Chaudhuri and Satish Rao. Learning Mixtures of Product Distributions Using Correlations and Independence
- Kamalika Chaudhuri and Satish Rao. Beyond Gaussians: Spectral Methods for Learning Mixtures of Heavy-Tailed Distributions
- Shai Ben-David, Tyler Lu and Dávid Pál. Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning
- Maria-Florina Balcan, Steve Hanneke and Jennifer Wortman. The True Sample Complexity of Active Learning (best student paper)
10:15-10:40 Coffee Break
10:40-11:40 Invited Talk
Peter Grünwald. The Catch-Up Phenomenon in Bayesian Inference11:45-13:00 On-Line Learning (I)
- 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
- Subhash Khot and Ashok Kumar Ponnuswami. Minimizing Wide Range Regret with Time Selection Functions
13:00-14:30 Lunch Break
14:30-15:30 Invited Talk
Robin Hanson. Combinatorial Prediction Markets15:35-16:50 Other Directions (I)
- Nir Ailon and Mehryar Mohri. An Efficient Reduction of Ranking to Classification
- Michael Kearns and Jennifer Wortman. Learning from Collective Behavior
- Bharath Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert Lanckriet and Bernhard Schölkopf. Injective Hilbert Space Embeddings of Probability Measures
16:50-17:15 Coffee Break
17:15-18:30 Complexity and Boolean Functions (I)
- 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
18:30-19:00 Open Problem Session
- Vitaly Feldman and Leslie Valiant. The Learning Power of Evolution
- Parikshit Gopalan, Adam Kalai and Adam Klivans. A Query Algorithm for Agnostically Learning DNF?
- Adam Smith and Manfred Warmuth. Learning Rotations
19:00-20:00 Break (UAI Posters)
20:00-21:30 COLT Business Meeting
Friday, July 11
8:35-10:15 Complexity and Boolean Functions (II)
- 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
10:15-10:40 Coffee Break
10:40-11:40 Invited Talk
Gábor Lugosi. Concentration inequalities11:45-13:00 Generalization and Statistics (I)
- Alon Zakai and Ya'acov Ritov. How Local Should a Learning Method Be?
- Yiming Ying and Colin Campbell. Learning Coordinate Gradients with Multi-Task Kernels
- Vladimir Koltchinskii and Ming Yuan. Sparse Recovery in Large Ensembles of Kernel Machines
13:00-14:30 Lunch Break
14:30-15:30 Invited Talk
Dan Klein. Unsupervised Learning for Natural Language Processing15:35-16:50 On-Line Learning and Bandits
- Amy Greenwald, Zheng Li and Warren Schudy. More Efficient Internal-Regret-Minimizing Algorithms
- Giovanni Cavallanti, Nicolò Cesa-Bianchi and Claudio Gentile. Linear Algorithms for Online Multitask Classification
- Jacob Abernethy, Elad Hazan and Alexander Rakhlin. Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization (best student paper)
16:50-17:15 Coffee Break
17:15-18:55 Other Directions (II)
- Wouter Koolen and Steven de Rooij. Combining Expert Advice Efficiently
- 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 Shalev-Shwartz and Yoram Singer. On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms
18:55-19:55 Rump Session
20:00-23:00 Banquet
Saturday, July 12
8:35-10:15 Bandits and Reinforcement Learning
- Andrey Bernstein and Nahum Shimkin. Adaptive Aggregation for Reinforcement Learning with Efficient Exploration: Deterministic Domains
- Peter Bartlett, Varsha Dani, Thomas Hayes, Sham Kakade, Alexander Rakhlin and Ambuj Tewari. High-Probability Regret Bounds for Bandit Online Linear Optimization
- Aleksandrs Slivkins and Eli Upfal. Adapting to a Changing Environment: the Brownian Restless Bandits
- Varsha Dani, Thomas Hayes and Sham Kakade. Stochastic Linear Optimization under Bandit Feedback
10:15-10:40 Coffee Break
10:40-12:20 Unsupervised and Semi-Supervised Learning
- Ohad Shamir and Naftali Tishby. Model Selection and Stability in k-means Clustering
- Shai Ben-David and Ulrike von Luxburg. Relating Clustering Stability to Properties of Cluster Boundaries
- Kamalika Chaudhuri and Andrew McGregor. Finding Metric Structure in Information Theoretic Clustering
- Karthik Sridharan and Sham Kakade. An Information Theoretic Framework for Multi-view Learning
12:20-13:50 Lunch Break
13:50-15:30 Online Learning (II)
- Jacob Abernethy, Peter Bartlett, Alexander Rakhlin and Ambuj Tewari. Optimal Stragies and Minimax Lower Bounds for Online Convex Games
- Robert Kleinberg, Alexandru Niculescu-Mizil and Yogeshwer Sharma. Regret Bounds for Sleeping Experts and Bandits (best student paper)
- Jacob Abernethy, Manfred Warmuth and Joel Yellin. Optimal Strategies for Random Walks
- András György, Gábor Lugosi and György Ottucsák. On-line Sequential Bin Packing
15:30-15:55 Coffee Break
15:55-18:00 Generalization and Statistics (II)
- Shuheng Zhou, John Lafferty and Larry Wasserman. Time Varying Undirected Graphs
- Constantine Caramanis and Shie Mannor. Learning in the Limit with Adversarial Disturbances
- Liwei Wang, Masashi Sugiyama, Cheng Yang, Zhi-Hua Zhou and Jufu Feng. On the Margin Explanation of Boosting Algorithms
- Aarti Singh, Robert Nowack and Clayton Scott. Adaptive Hausdorff Estimation of Density Level Sets
- Satyaki Mahalanabis and Daniel Stefankovic. Density Estimation in Linear Time