Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds

TL;DR: Under this framework, an algorithm is designed that constructs the variance-aware confidence set based on empirical risk minimization and proves a variance-dependent regret bound for generalized linear bandits, and an algorithm based on follow-the-regularized-leader (FTRL) subroutine and online-to-confidence-set conversion which can achieve a tighter variance- dependent regret under certain conditions.

Abstract: We consider learning a stochastic bandit model, where the reward function belongs to a general class of uniformly bounded functions, and the additive noise can be heteroscedastic. Our model captures contextual linear bandits and generalized linear bandits as special cases. While previous works (Kirschner and Krause, 2018; Zhou et al., 2021) based on weighted ridge regression can deal with linear bandits with heteroscedastic noise, they are not directly applicable to our general model due to the curse of nonlinearity. In order to tackle this problem, we propose a multi-level learning framework for the general bandit model. The core idea of our framework is to partition the observed data into different levels according to the variance of their respective reward and perform online learning at each level collaboratively. Under our framework, we first design an algorithm that constructs the variance-aware confidence set based on empirical risk minimization and prove a variance-dependent regret bound. For generalized linear bandits, we further propose an algorithm based on follow-the-regularized-leader (FTRL) subroutine and online-to-confidence-set conversion, which can achieve a tighter variance-dependent regret under certain conditions.