2 papers
stat.ML2024
Sliding-Window Thompson Sampling for Non-Stationary Settings
Marco Fiandri, Alberto Maria Metelli, Francesco Trovò
Non-stationary multi-armed bandits (NS-MABs) model sequential decision-making problems in which the expected rewards of a set of actions, a.k.a.~arms, evolve over time. In this pap…
cs.LG2016
Unimodal Thompson Sampling for Graph-Structured Arms
Stefano Paladino, Francesco Trovò, Marcello Restelli +1
We study, to the best of our knowledge, the first Bayesian algorithm for unimodal Multi-Armed Bandit (MAB) problems with graph structure. In this setting, each arm corresponds to a…