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 lear...
arXiv:2609.13440v1 Announce Type: new
Abstract: We give a deterministic algorithm for online inverse linear optimization with regret $O(d)$, uniform in the horizon and $O(d^{2})$ time per round. A bo...
By Yang Cai, Anupam Gupta, Vineet Gupta, Guru Guruganesh, Yanchen Jiang, Christopher Liaw, Aranyak Mehta, Renato Paes Leme, Grigoris Velegkas, Di Wang
arXiv:2609. 26978v1 Announce Type: cross Abstract: We study online inverse linear optimization with a fixed unknown linear utility: in each round, an environment presents a compact action set, the learner recommends an action from it, and the environment returns an action that maximizes the utility over the same set.
By Shinsaku Sakaue
arXiv:2610.20416v1 Announce Type: new
Abstract: For the fundamental problem of prediction from expert advice, Freund asked whether one could compete with a comparator that switches $k$ times among on...
By Angelos Assos, Anand Brahmbhatt, Elad Hazan
arXiv:2603. 28201v3 Announce Type: replace Abstract: We revisit the standard perturbation-based approach of Abernethy et al.
By Andrew Jacobsen, Dorian Baudry, Shinji Ito, Nicol\`o Cesa-Bianchi
arXiv:2505. 21460v2 Announce Type: replace Abstract: We study online calibration of multi-dimensional forecasts over an arbitrary convex set $P \subset \mathbb{R}^d$ relative to an arbitrary norm $|\cdot|$.
By Maxwell Fishelson, Noah Golowich, Mehryar Mohri, Jon Schneider