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ORCID22nd COLT 2009: Montreal, Quebec, Canada
- COLT 2009 - The 22nd Conference on Learning Theory, Montreal, Quebec, Canada, June 18-21, 2009. 2009
Algorithms I
- Adam Tauman Kalai, Ravi Sastry:
The Isotron Algorithm: High-Dimensional Isotonic Regression. - Nader H. Bshouty, Philip M. Long:
Linear Classifiers are Nearly Optimal When Hidden Variables Have Diverse Effect. - Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh:
Domain Adaptation: Learning Bounds and Algorithms. - Nader H. Bshouty:
Optimal Algorithms for the Coin Weighing Problem with a Spring Scale.
Online Learning I
- Eyal Even-Dar, Robert Kleinberg, Shie Mannor, Yishay Mansour:
Online Learning for Global Cost Functions. - Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims:
The K-armed Dueling Bandits Problem. - Gábor Lugosi, Omiros Papaspiliopoulos, Gilles Stoltz:
Online Multi-task Learning with Hard Constraints.
Sparsity and Algorithms
- Karim Lounici, Massimiliano Pontil, Alexandre B. Tsybakov, Sara A. van de Geer:
Taking Advantage of Sparsity in Multi-Task Learning. - Arnak S. Dalalyan, Alexandre B. Tsybakov:
Sparse Regression Learning by Aggregation and Langevin Monte-Carlo. - Sivan Sabato, Naftali Tishby:
Homogeneous Multi-Instance Learning with Arbitrary Dependence. - Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
A Spectral Algorithm for Learning Hidden Markov Models.
Generalization I
- Andreas Maurer, Massimiliano Pontil:
Empirical Bernstein Bounds and Sample-Variance Penalization. - Mark D. Reid, Robert C. Williamson:
Generalised Pinsker Inequalities. - Sanjay Jain, Frank Stephan:
Consistent Partial Identification.
Algorithms II
- Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale:
Fast and Optimal Prediction on a Labeled Tree. - Mark Herbster, Guy Lever:
Predicting the Labelling of a Graph via Minimum $p$-Seminorm Interpolation. - Maria-Florina Balcan, Mark Braverman:
Finding Low Error Clusterings. - Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Stochastic Convex Optimization.
Dimensionality and Optimization
- Samory Kpotufe:
Escaping the Curse of Dimensionality with a Tree-based Regressor. - Hariharan Narayanan, Partha Niyogi:
On the Sample Complexity of Learning Smooth Cuts on a Manifold. - Hans Ulrich Simon, Nikolas List:
SVM-Optimization and Steepest-Descent Line Search.
Bandits
- Jean-Yves Audibert, Sébastien Bubeck:
Minimax Policies for Adversarial and Stochastic Bandits. - H. Brendan McMahan, Matthew J. Streeter:
Tighter Bounds for Multi-Armed Bandits with Expert Advice. - Nicolò Cesa-Bianchi, Gábor Lugosi:
Combinatorial Bandits. - Jacob D. Abernethy, Alexander Rakhlin:
Beating the Adaptive Bandit with High Probability. - Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin:
A Stochastic View of Optimal Regret through Minimax Duality.
Complexity I
- Jeffrey C. Jackson, Karl Wimmer:
New Results for Random Walk Learning. - Vitaly Feldman:
Robustness of Evolvability. - Hayato Kobayashi, Ayumi Shinohara:
Complexity of Teaching by a Restricted Number of Examples. - Luis Rademacher, Navin Goyal:
Learning Convex Bodies is Hard.
Noise
- Adam Tauman Kalai, Varun Kanade, Yishay Mansour:
Reliable Agnostic Learning. - Shai Ben-David, Dávid Pál, Shai Shalev-Shwartz:
Agnostic Online Learning. - Alessandro Lazaric, Rémi Munos:
Hybrid Stochastic-Adversarial On-line Learning.
Active Learning and Stability
- Eric Friedman:
Active Learning for Smooth Problems. - Steve Hanneke:
Adaptive Rates of Convergence in Active Learning. - Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Learnability and Stability in the General Learning Setting. - Ofer Dekel, Ohad Shamir:
Vox Populi: Collecting High-Quality Labels from a Crowd.
Generalization II
- Ingo Steinwart, Don R. Hush, Clint Scovel:
Optimal Rates for Regularized Least Squares Regression. - Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito:
A Note on Learning with Integral Operators. - Yiming Ying, Colin Campbell:
Generalization Bounds for Learning the Kernel Problem.
Open Problems
- Jacob D. Abernethy, Alexander Rakhlin:
An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction?. - Maria-Florina Balcan:
Better Guarantees for Sparsest Cut Clustering. - Jacob D. Abernethy, Manfred K. Warmuth:
Minimax Games with Bandits. - Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan:
The Complexity of Improperly Learning Large Margin Halfspaces.
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