139 citations · 615 across the 107 of their papers we have counts for
43 papers · 1 filter
Approximating Optimal Policies in Heterogeneous Asynchronous Multi-player Bandits
Sacha Arroues-Paykin, Etienne Boursier, Vianney Perchet
Motivated by applications to cognitive radio networks, multi-player multi-armed bandits (MMAB) have attracted considerable attention in recent years. The asynchronous variant, in w…
Learning When to Automate: Queue Control in Human-AI Service Systems
Giovanni Montanari, Marco Scarsini, Vianney Perchet
We study a human-AI service system in which tasks arrive sequentially and are processed through a two-stage architecture: an automated chatbot followed, when necessary, by a human…
Asymptotically Optimal Learning for Parametric Prophet Inequalities
Jung-hun Kim, Anna Grebennikova, Vianney Perchet
We study learning in prophet inequalities with i.i.d. rewards drawn from an exponential-type parametric family with an unknown parameter , a class that includes exponential, Par…
Online Packet Scheduling with Deadlines and Learning
Gianmarco Genalti, Achraf Azize, Vianney Perchet
Network routers that enforce Quality-of-Service (QoS) guarantees must decide, at every clock cycle, which expiring packet of information to transmit, even when the value of the pac…
Adaptive Bandit Algorithms for Contextual Matching Markets
Shiyun Lin, Simon Mauras, Vianney Perchet +1
We study bandit learning in matching markets, where players and arms constitute the two market sides, and the players' utilities are linear in the arm contexts. In each round, new…
The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
Côme Fiegel, Pierre Ménard, Tadashi Kozuno +2
We study the problem of learning in zero-sum matrix games with repeated play and bandit feedback. Specifically, we focus on developing uncoupled algorithms that guarantee, without…