Meta Learning in Bandits within Shared Affine Subspaces

arXiv:2404.00688v1 Announce Type: new
Abstract: We study the problem of meta-learning several contextual stochastic bandits tasks by leveraging their concentration around a low-dimensional affine subspace, which we learn via online principal component analysis to reduce the expected regret over the encountered bandits. We propose and theoretically analyze two strategies that solve the problem: One based on the principle of optimism in the face of uncertainty and the other via Thompson sampling. Our framework is generic and includes previously proposed approaches …

abstract analysis analyze arxiv cs.lg learn low meta meta-learning reduce solve stat.ml stochastic strategies study tasks type via