Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs

Abstract: In this paper, we study the problem of stochastic linear bandits with finite action sets. Most of existing work assume the payoffs are bounded or sub-Gaussian, which may be violated in some scenarios such as financial markets. To settle this issue, we analyze the linear bandits with heavy-tailed payoffs, where the payoffs admit finite $1+\epsilon$ moments for some $\epsilon\in(0,1]$. Through median of means and dynamic truncation, we propose two novel algorithms which enjoy a sublinear regret bound of $$\widetilde{O}(d<sup>{\frac{1}{2}}T<sup>{\frac{1}{1+\epsilon}})$$, where $d$ is the dimension of contextual information and $T$ is the time horizon. Meanwhile, we provide an $$\Omega(d<sup>{\frac{\epsilon}{1+\epsilon}}T<sup>{\frac{1}{1+\epsilon}})$$ lower bound, which implies our upper bound matches the lower bound up to polylogarithmic factors in the order of $d$ and $T$ when $\epsilon=1$. Finally, we conduct numerical experiments to demonstrate the effectiveness of our algorithms and the empirical results strongly support our theoretical guarantees.