9 citations · 10 across the 2 of their papers we have counts for
2 papers
cs.LG2021★ 1 cited
A High Performance, Low Complexity Algorithm for Multi-Player Bandits Without Collision Sensing Information
Cindy Trinh, Richard Combes
Motivated by applications in cognitive radio networks, we consider the decentralized multi-player multi-armed bandit problem, without collision nor sensing information. We propose…
stat.ML2019★ 9 cited
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling
Cindy Trinh, Emilie Kaufmann, Claire Vernade +1
Stochastic Rank-One Bandits (Katarya et al, (2017a,b)) are a simple framework for regret minimization problems over rank-one matrices of arms. The initially proposed algorithms are…