22. 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.

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.

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 Hsu, Sham M. Kakade, Tong Zhang:
A Spectral Algorithm for Learning Hidden Markov Models.

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.

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.

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.

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 Abernethy, Alexander Rakhlin:
Beating the Adaptive Bandit with High Probability.
Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin:
A Stochastic View of Optimal Regret through Minimax Duality.

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.

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.

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.

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.

Jacob 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 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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