Adaptive Clustering and Personalization in Multi-Agent Stochastic Linear Bandits. (arXiv:2106.08902v2 [stat.ML] UPDATED)

We consider the problem of minimizing regret in an $N$ agent heterogeneous
stochastic linear bandits framework, where the agents (users) are similar but
not all identical. We model user heterogeneity using two popularly used ideas
in practice; (i) A clustering framework where users are partitioned into groups
with users in the same group being identical to each other, but different
across groups, and (ii) a personalization framework where no two users are
necessarily identical, but a user's parameters are close …

arxiv clustering ml personalization stochastic