Bandit Convex Optimisation Revisited: FTRL Achieves $\tilde{O}(t^{1/2})$ Regret
Machine Learning
2023-06-27 v2
Abstract
We show that a kernel estimator using multiple function evaluations can be easily converted into a sampling-based bandit estimator with expectation equal to the original kernel estimate. Plugging such a bandit estimator into the standard FTRL algorithm yields a bandit convex optimisation algorithm that achieves regret against adversarial time-varying convex loss functions.
Cite
@article{arxiv.2302.00358,
title = {Bandit Convex Optimisation Revisited: FTRL Achieves $\tilde{O}(t^{1/2})$ Regret},
author = {David Young and Douglas Leith and George Iosifidis},
journal= {arXiv preprint arXiv:2302.00358},
year = {2023}
}
Comments
Error in proof