42 citations · 43 across the 6 of their papers we have counts for
6 papers
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…
Sequential Counterfactual Risk Minimization
Houssam Zenati, Eustache Diemert, Matthieu Martin +2
Counterfactual Risk Minimization (CRM) is a framework for dealing with the logged bandit feedback problem, where the goal is to improve a logging policy using offline data. In this…
Reimagining Demand-Side Management with Mean Field Learning
Bianca Marin Moreno, Margaux Brégère, Pierre Gaillard +1
Integrating renewable energy into the power grid while balancing supply and demand is a complex issue, given its intermittent nature. Demand side management (DSM) offers solutions…
Counterfactual Learning of Stochastic Policies with Continuous Actions
Houssam Zenati, Alberto Bietti, Matthieu Martin +3
Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare. In this paper, we address the problem of le…
Mirror Descent Meets Fixed Share (and feels no regret)
Nicolò Cesa-Bianchi, Pierre Gaillard, Gabor Lugosi +1
Mirror descent with an entropic regularizer is known to achieve shifting regret bounds that are logarithmic in the dimension. This is done using either a carefully designed project…