arXiv Machine Learning
Jun 15

Online Convex Optimization with Sublinear Noisy Probes

arXiv:2606. 14640v1 Announce Type: new Abstract: We study Online Convex Optimization (OCO) over a convex set $K\subseteq \mathbb R^d$, where in each round $t$ the learner selects $x_t\in K$ and then observes a convex loss $f_t:K\to[0,1]$, with the goal of minimizing regret to the best fixed decision in hindsight.

By Simone Di Gregorio, Anupam Gupta, Stefano Leonardi, Matteo Russo