31 citations · 162 across the 25 of their papers we have counts for
27 papers
The Bias of Nonlinear Two-Time-scale Stochastic Approximation under Constant Step-Sizes
Djamel Rassem Lamouri, Dorian Baudry, Nicolas Gast
Two-timescale stochastic approximation (TTSA) is a fundamental tool for analyzing coupled iterative algorithms in reinforcement learning, optimization, and stochastic control. Howe…
Trading-off price for data quality to achieve fair online allocation
Mathieu Molina, Nicolas Gast, Patrick Loiseau +1
We consider the problem of online allocation subject to a long-term fairness penalty. Contrary to existing works, however, we do not assume that the decision-maker observes the pro…
Decentralized model-free reinforcement learning in stochastic games with average-reward objective
Romain Cravic, Nicolas Gast, Bruno Gaujal
We propose the first model-free algorithm that achieves low regret performance for decentralized learning in two-player zero-sum tabular stochastic games with infinite-horizon aver…
Bias and Refinement of Multiscale Mean Field Models
Sebastian Allmeier, Nicolas Gast
Mean field approximation is a powerful technique which has been used in many settings to study large-scale stochastic systems. In the case of two-timescale systems, the approximati…
Reoptimization Nearly Solves Weakly Coupled Markov Decision Processes
Nicolas Gast, Bruno Gaujal, Chen Yan
We propose a new policy, called the LP-update policy, to solve finite horizon weakly-coupled Markov decision processes. The latter can be seen as multi-constraint multi-action band…
Fairness in Selection Problems with Strategic Candidates
Vitalii Emelianov, Nicolas Gast, Patrick Loiseau
To better understand discriminations and the effect of affirmative actions in selection problems (e.g., college admission or hiring), a recent line of research proposed a model bas…