COLT 2009

44 papers

A Note on Learning with Integral Operators Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito

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

A Stochastic View of Optimal Regret Through Minimax Duality Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin

Active Learning for Smooth Problems Eric Friedman

Adaptive Rates of Convergence in Active Learning Steve Hanneke

Agnostic Online Learning Shai Ben-David, Dávid Pál, Shai Shalev-Shwartz

An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction? Jacob D. Abernethy, Alexander Rakhlin

Beating the Adaptive Bandit with High Probability Jacob D. Abernethy, Alexander Rakhlin

Better Guarantees for Sparsest Cut Clustering Maria-Florina Balcan

Combinatorial Bandits Nicolò Cesa-Bianchi, Gábor Lugosi

Complexity of Teaching by a Restricted Number of Examples Hayato Kobayashi, Ayumi Shinohara

Consistent Partial Identification Sanjay Jain, Frank Stephan

Domain Adaptation: Learning Bounds and Algorithms Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh

Empirical Bernstein Bounds and Sample-Variance Penalization Andreas Maurer, Massimiliano Pontil

Escaping the Curse of Dimensionality with a Tree-Based Regressor Samory Kpotufe

Fast and Optimal Prediction on a Labeled Tree Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale

Finding Low Error Clusterings Maria-Florina Balcan, Mark Braverman

Generalised Pinsker Inequalities Mark D. Reid, Robert C. Williamson

Generalization Bounds for Learning the Kernel Problem Yiming Ying, Colin Campbell

Homogeneous Multi-Instance Learning with Arbitrary Dependence Sivan Sabato, Naftali Tishby

Hybrid Stochastic-Adversarial On-Line Learning Alessandro Lazaric, Rémi Munos

Learnability and Stability in the General Learning Setting Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan

Learning Convex Bodies Is Hard Luis Rademacher, Navin Goyal

Linear Classifiers Are Nearly Optimal When Hidden Variables Have Diverse Effect Nader H. Bshouty, Philip M. Long

Minimax Games with Bandits Jacob D. Abernethy, Manfred K. Warmuth

Minimax Policies for Adversarial and Stochastic Bandits Jean-Yves Audibert, Sébastien Bubeck

New Results for Random Walk Learning Jeffrey C. Jackson, Karl Wimmer

On the Sample Complexity of Learning Smooth Cuts on a Manifold Hariharan Narayanan, Partha Niyogi

Online Learning for Global Cost Functions Eyal Even-Dar, Robert Kleinberg, Shie Mannor, Yishay Mansour

Online Multi-Task Learning with Hard Constraints Gábor Lugosi, Omiros Papaspiliopoulos, Gilles Stoltz

Optimal Algorithms for the Coin Weighing Problem with a Spring Scale Nader H. Bshouty

Optimal Rates for Regularized Least Squares Regression Ingo Steinwart, Don R. Hush, Clint Scovel

Predicting the Labelling of a Graph via Minimum $p$-Seminorm Interpolation Mark Herbster, Guy Lever

Reliable Agnostic Learning Adam Tauman Kalai, Varun Kanade, Yishay Mansour

Robustness of Evolvability Vitaly Feldman

Sparse Regression Learning by Aggregation and Langevin Monte-Carlo Arnak S. Dalalyan, Alexandre B. Tsybakov

Stochastic Convex Optimization Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan

SVM-Optimization and Steepest-Descent Line Search Hans Ulrich Simon, Nikolas List

Taking Advantage of Sparsity in Multi-Task Learning Karim Lounici, Massimiliano Pontil, Alexandre B. Tsybakov, Sara A. van de Geer

The Complexity of Improperly Learning Large Margin Halfspaces Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan

The Isotron Algorithm: High-Dimensional Isotonic Regression Adam Tauman Kalai, Ravi Sastry

The K-Armed Dueling Bandits Problem Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims

Tighter Bounds for Multi-Armed Bandits with Expert Advice H. Brendan McMahan, Matthew J. Streeter

Vox Populi: Collecting High-Quality Labels from a Crowd Ofer Dekel, Ohad Shamir