COLT 2009 Sessions
Friday, June 19
| 8:35 - 10:15 | Algorithms I (Session chair: Claudio Gentile) | |
| The Isotron Algorithm: High-Dimensional Isotonic Regression. Adam Kalai and Ravi Sastry. Linear classifiers are nearly optimal when hidden variables have diverse effect. Nader H. Bshouty and Phil Long. Domain Adaptation: Learning Bounds and Algorithms. Yishay Mansour, Mehryar Mohri and Afshin Rostamizadeh. Optimal Algorithms for the Coin Weighing Problem with a Spring Scale. Nader Bshouty. |
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| 10:15 - 10:40 | Coffee break | |
| 10:40 - 11:40 | Invited talk (Session chair: Gabor Lugosi) | |
| Towards Agnostic and Interactive Machine Learning. Adam Tauman Kalai | ||
| 11:45 - 13:00 | Online Learning I (Session chair: Sasha Rakhlin) | |
| Online Learning for Global Cost Functions.
Eyal Even-Dar, Robert Kleinberg, Shie Mannor and Yishay Mansour. The K-armed Dueling Bandits Problem. Yisong Yue, Josef Broder, Robert Kleinberg and Thorsten Joachims. Online Multi-task Learning with Hard Constraints. Gabor Lugosi, Omiros Papaspiliopoulos and Gilles Stoltz. |
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| 13:00 - 14:30 | Lunch break (on your own) | |
| 14:30 - 16:15 | Sparsity and Algorithms (Session chair: Shai Shalev-Shwartz) | |
| Taking Advantage of Sparsity in Multi-Task Learning.
Karim Lounici, Massimiliano Pontil, Alexandre B. Tsybakov and Sara A. van de Geer. Sparse Regression Learning by Aggregation and Langevin Monte-Carlo. Arnak Dalalyan and Alexandre Tsybakov. Homogeneous Multi-Instance Learning with Arbitrary Dependence. Sivan Sabato and Naftali Tishby. A Spectral Algorithm for Learning Hidden Markov Models. Daniel Hsu, Sham M. Kakade and Tong Zhang. |
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| 16:15 - 16:40 | Coffee break | |
| 16:40 - 17:55 | Generalization I (Session chair: Sandra Zilles) | |
| Empirical Bernstein Bounds and Sample-Variance Penalization.
Andreas Maurer and Massimiliano Pontil. Generalised Pinsker Inequalities. Mark Reid and Robert Williamson. Consistent Partial Identification. Sanjay Jain and Frank Stephan. |
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| 18:00 - 20:00 | Dinner break (on your own) | |
| 20:00 - 20:40 | Open problems session (Session chair: Csaba Szepesvari) | |
| An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction.
Jacob Abernethy and Alexander Rakhlin. Better Guarantees for Sparsest Cut Clustering. Maria-Florina Balcan Minimax Games with Bandits. Jacob Abernethy and Manfred Warmuth. The Complexity of Improperly Learning Large Margin Halfspaces. Shai Shalev-Shwartz, Ohad Shamir and Karthik Sridharan. |
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| 20:40 - 22:00 | Business meeting |
Saturday, June 20
| 8:35 - 10:15 | Algorithms II (Session chair: Jacob Abernethy) | |
| Fast and Optimal Prediction on a Labeled Tree.
Nicolo Cesa-Bianchi, Claudio Gentile and Fabio Vitale. Predicting the Labelling of a Graph via Minimum $p$-Seminorm Interpolation. Mark Herbster and Guy Lever. Finding low error clusterings. Maria Florina Balcan and Mark Braverman. Stochastic Convex Optimization. Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro and Karthik Sridharan. |
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| 10:15 - 10:40 | Coffee break | |
| 10:40 - 11:40 | Invited talk. (Session chair: Sanjoy Dasgupta) | |
| Sparse recovery using sparse matrices.. Piotr Indyk | ||
| 11:45 - 13:00 | Dimensionality and Optimization (Session chair: Steve Hanneke) | |
| Escaping the curse of dimensionality with a tree-based regressor. Samory Kpotufe. On the sample complexity of learning smooth cuts on a manifold. Hariharan Narayanan and Partha Niyogi. SVM-Optimization and Steepest-Descent Line Search. Hans Simon and Nikolas List. |
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| 13:00 - 14:30 | Lunch break (on your own) | |
| 14:30 - 16:40 | Bandits (Session chair: Eyal Even-Dar) | |
| Minimax policies for adversarial and stochastic bandits.
Jean-Yves Audibert and Sebastien Bubeck. Tighter Bounds for Multi-Armed Bandits with Expert Advice. H. Brendan McMahan and Matthew Streeter. Combinatorial Bandits. Nicolo Cesa-Bianchi and Gabor Lugosi. Beating the Adaptive Bandit with High Probability. Jacob Abernethy and Alexander Rakhlin. A Stochastic View of Optimal Regret through Minimax Duality. Jacob Abernethy, Alekh Agarwal, Peter Bartlett and Alexander Rakhlin. |
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| 16:40 - 17:10 | Buses to banquet | |
| 17:10 - 21:30 | Tour around Fort Chambly, followed by banquet |
Sunday, June 21
| 8:35 - 10:15 | Complexity (Session chair: Nader Bshouty) | |
| New results for random walk learning. Jeffrey Jackson and Karl Wimmer. Robustness of Evolvability. Vitaly Feldman. Complexity of Teaching by a Restricted Number of Examples. Hayato Kobayashi and Ayumi Shinohara. Learning convex bodies is hard. Luis Rademacher and Navin Goyal. |
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| 10:15 - 10:40 | Coffee break | |
| 10:40 - 11:40 | Impromptu session (Session chair: Jeffrey Jackson) | |
| 11:45 - 13:00 | Noise (Session chair: Sham Kakade) | |
| Reliable Agnostic Learning. Adam Tauman Kalai, Varun Kanade and Yishay Mansour. Agnostic Online Learning. Shai Ben-David, David Pal and Shai Shalev-Shwartz. Hybrid Stochastic-Adversarial On-line Learning. Alessandro Lazaric and Remi Munos. |
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| 13:00 - 14:30 | Lunch break (on your own) | |
| 14:30 - 16:15 | Active Learning and Stability (Session chair: Shai Ben-David) | |
| Active Learning for Smooth Problems. Eric Friedman Adaptive Rates of Convergence in Active Learning. Steve Hanneke. Learnability and Stability in the General Learning Setting. Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro and Karthik Sridharan. Vox Populi: Collecting High-Quality Labels from a Crowd. Ofer Dekel and Ohad Shamir. |
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| 16:15 - 16:40 | Coffee break | |
| 16:40 - 17:55 | Generalization II (Session chair: Shie Mannor) | |
| Optimal Rates for Regularized Least Squares Regression.
Ingo Steinwart, Don Hush and Clint Scovel. A Note on Learning with Integral Operators. Lorenzo Rosasco, Mikhail Belkin and Ernesto De Vito. Generalization Bounds for Learning the Kernel Problem. Yiming Ying and Colin Campbell. |
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| 18:00 | Conference ends |
