26 pagesInternational audienceWe consider combinatorial semi-bandits with uncorrelated Gaussian rewards. In this article, we propose the first method, to the best of our knowledge, that enables to compute the solution of the Graves-Lai optimization problem in polynomial time for many combinatorial structures of interest. In turn, this immediately yields the first known approach to implement asymptotically optimal algorithms in polynomial time for combinatorial semi-bandits
This paper investigates stochastic and adversarial combinatorial multi-armed bandit problems. In the...
International audienceThis paper investigates stochastic and adversarial combinatorial multi-armed b...
This document presents in a unified way different results about the optimal solution of several mult...
International audienceWe consider combinatorial semi-bandits over a set X ⊂ {0, 1} d where rewards a...
International audienceWe consider combinatorial semi-bandits over a set of arms X ⊂ {0, 1} d where r...
In this paper, we consider efficient learning in large-scale combinatorial semi-bandits with linear ...
Let Zmax and Zmin be respectively the maximum and minimum of the objective function in a combinatori...
A stochastic combinatorial semi-bandit is an on-line learning problem where at each step a learn-ing...
We define a class of zero-sum games with combinatorial structure, where the best response problem of...
International audienceWe improve the efficiency of algorithms for stochastic combinatorial semi-band...
We investigate the piecewise-stationary combinatorial semi-bandit problem. Compared to the original ...
Abstract. Our main result is an efficient construction of a hitting set generator against the class ...
Inspired by recent results on polynomial time reinforcement algorithms that accumulate near-optimal ...
We propose a theoretical and computational framework for approximating the optimal policy in multi-a...
We propose a novel theoretical characterization of the optimal “Gittins index ” policy in multi-arme...
This paper investigates stochastic and adversarial combinatorial multi-armed bandit problems. In the...
International audienceThis paper investigates stochastic and adversarial combinatorial multi-armed b...
This document presents in a unified way different results about the optimal solution of several mult...
International audienceWe consider combinatorial semi-bandits over a set X ⊂ {0, 1} d where rewards a...
International audienceWe consider combinatorial semi-bandits over a set of arms X ⊂ {0, 1} d where r...
In this paper, we consider efficient learning in large-scale combinatorial semi-bandits with linear ...
Let Zmax and Zmin be respectively the maximum and minimum of the objective function in a combinatori...
A stochastic combinatorial semi-bandit is an on-line learning problem where at each step a learn-ing...
We define a class of zero-sum games with combinatorial structure, where the best response problem of...
International audienceWe improve the efficiency of algorithms for stochastic combinatorial semi-band...
We investigate the piecewise-stationary combinatorial semi-bandit problem. Compared to the original ...
Abstract. Our main result is an efficient construction of a hitting set generator against the class ...
Inspired by recent results on polynomial time reinforcement algorithms that accumulate near-optimal ...
We propose a theoretical and computational framework for approximating the optimal policy in multi-a...
We propose a novel theoretical characterization of the optimal “Gittins index ” policy in multi-arme...
This paper investigates stochastic and adversarial combinatorial multi-armed bandit problems. In the...
International audienceThis paper investigates stochastic and adversarial combinatorial multi-armed b...
This document presents in a unified way different results about the optimal solution of several mult...