9 citations · 14 across the 11 of their papers we have counts for
11 papers
Leveraging Similarities in Multi-Armed Bandits
Khaled Eldowa, Thibaud Rahier, Augustin Cablant +2
In many online learning and bandit problems, the actions we consider possess inherent similarities--for instance because they share latent traits, tags, or hierarchical structure.…
Functional Natural Policy Gradients
Aurelien Bibaut, Houssam Zenati, Thibaud Rahier +1
We propose a cross-fitted debiasing device for policy learning from offline data. A key consequence of the resulting learning principle is regret even for policy classes…
Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit
Julien Zhou, Pierre Gaillard, Thibaud Rahier +1
We address the online unconstrained submodular maximization problem (Online USM), in a setting with stochastic bandit feedback. In this framework, a decision-maker receives noisy r…
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits
Julien Zhou, Pierre Gaillard, Thibaud Rahier +2
We address the problem of stochastic combinatorial semi-bandits, where a player selects among P actions from the power set of a set containing d base items. Adaptivity to the probl…
Maximizing the Success Probability of Policy Allocations in Online Systems
Artem Betlei, Mariia Vladimirova, Mehdi Sebbar +3
The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users. The bidding procedure is highly c…
Nested bandits
Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier +1
In many online decision processes, the optimizing agent is called to choose between large numbers of alternatives with many inherent similarities; in turn, these similarities imply…