Paper recorded by Signals 4 on 2026-10-06 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07
Category: cs.LG · 机器学习 · first seen 2026-10-07
In online inverse linear optimization, a learner recommends an action and then observes the choice of an expert who maximizes a fixed, unknown linear objective on $\mathbb{R}^{d}$; the goal is to learn to optimize this objective without observing it. Sakaue recently obtained the optimal regret $O(\sqrt d)$ with a randomized algorithm making $(dT)^{O(d)}$ linear optimizations per round, and asked w