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Covariance Adaptive Best Arm Identification
This framework allows the learner to estimate the covariance among the arms distributions, enabling a more efficient identification of the best arm.
Active Ranking of Experts Based on their Performances in Many Tasks
We consider the problem of ranking n experts based on their performances on d tasks.
Constant regret for sequence prediction with limited advice
In the standard multi-armed bandits setting, when the learner is allowed to play only one expert per round and observe only its feedback, known optimal regret bounds are of the order O($\sqrt$ KT).
Fast rates for prediction with limited expert advice
We investigate the problem of minimizing the excess generalization error with respect to the best expert prediction in a finite family in the stochastic setting, under limited access to information.
Online Orthogonal Matching Pursuit
Greedy algorithms for feature selection are widely used for recovering sparse high-dimensional vectors in linear models.
