Improved Regret for Bandit Convex Optimization with Delayed Feedback

arXiv:2402.09152v1 Announce Type: new
Abstract: We investigate bandit convex optimization (BCO) with delayed feedback, where only the loss value of the action is revealed under an arbitrary delay. Previous studies have established a regret bound of $O(T^{3/4}+d^{1/3}T^{2/3})$ for this problem, where $d$ is the maximum delay, by simply feeding delayed loss values to the classical bandit gradient descent (BGD) algorithm. In this paper, we develop a novel algorithm to enhance the regret, which carefully exploits the delayed bandit feedback via …

abstract arxiv cs.lg delay feedback loss optimization studies type value