@inproceedings{liu2026lipschitz,
author = {Liu, Zhongxuan and Kang, Yue and Lee, Thomas C. M.},
title = {Lipschitz Bandits with Stochastic Delayed Feedback},
booktitle = {ICLR 2026},
year = {2026},
month = {April},
abstract = {The Lipschitz bandit problem extends stochastic bandits to a continuous action set defined over a metric space, where the expected reward function satisfies a Lipschitz condition. In this work, we introduce a new problem of Lipschitz bandit in the presence of stochastic delayed feedback, where the rewards are not observed immediately but after a random delay. We consider both bounded and unbounded stochastic delays, and design algorithms that attain sublinear regret guarantees in each setting. For bounded delays, we propose a delay-aware zooming algorithm that retains the optimal performance of the delay-free setting up to an additional term that scales with the maximum delay $\tau_\max$. For unbounded delays, we propose a novel phased learning strategy that accumulates reliable feedback over carefully scheduled intervals, and establish a regret lower bound showing that our method is nearly optimal up to logarithmic factors. Finally, we present experimental results to demonstrate the efficiency of our algorithms under various delay scenarios.},
url = {http://approjects.co.za/?big=en-us/research/publication/lipschitz-bandits-with-stochastic-delayed-feedback/},
}