SAAD , El Mehdi

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. We assume that the learner only has access to a limited number of expert advices per training round, as well as for prediction. Assuming that the...

We investigate the problem of cumulative regret minimization for individual sequence prediction with respect to the best expert in a finite family of size K under limited access to information. We assume that in each round, the learner can predict using a convex combination of at most p experts for prediction, then they can observe a posteriori...

We consider the problem of best arm identification in the multi-armed bandit model, under fixed confidence. Given a confidence input δ, the goal is to identify the arm with the highest mean reward with a probability of at least 1 − δ, while minimizing the number of arm pulls. While the literature provides solutions to this problem under the...

Greedy algorithms for feature selection are widely used for recovering sparse high-dimensional vectors in linear models. In classical procedures, the main emphasis was put on the sample complexity, with little or no consideration of the computation resources required. We present a novel online algorithm: Online Orthogonal Matching Pursuit...

We consider the problem of best arm identification in the multi-armed bandit model, under fixed confidence. Given a confidence input δ, the goal is to identify the arm with the highest mean reward with a probability of at least 1 − δ, while minimizing the number of arm pulls. While the literature provides solutions to this problem under the...