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 as special
cases. Besides, the empirical results show that our methods significantly
reduce the regret on several bandit tasks.

通过在线主成分分析学习低维仿射子空间，从而降低遇到的赌博机的预期遗憾，我们研究了通过充分利用其集中性解决多个情境随机赌博任务的元学习问题。我们提出并理论地分析了两种解决方法：一种是基于在不确定性面前的乐观原则，另一种是通过汤普森抽样。我们的框架是通用的，并包括之前提出的方法作为特殊情况。此外，实证结果表明，我们的方法显著减少了一些赌博任务的遗憾。