Hugging Face Trending Papers
Optimal and Efficient Online Inverse Optimization
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv Machine Learning
Oct 7
Optimal and Efficient Online Inverse Optimization
arXiv:2610. 08735v1 Announce Type: new Abstract: 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.
arXiv Machine Learning
Sep 24
Tight Regret Bound for Online Inverse Linear Optimization via Multiscale Matrix Weights
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.
arXiv Machine Learning
Sep 15
Efficient Online Inverse Optimization with $O(d)$ Regret
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...
arXiv Machine Learning
Oct 15
Tracking a Small Set of Experts in Online Convex Optimization
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...
arXiv Machine Learning
Oct 15
Dimension-Optimal Dynamic Regret in Exp-Concave Online Learning
arXiv:2610.20187v1 Announce Type: new Abstract: Tracking changing comparators is a central problem in online learning. For exp-concave losses, previous work established the $T^{1/3}P_T^{2/3}$ depende...
arXiv Machine Learning
Sep 22
Small Gradient Norm Regret for Online Convex Optimization
arXiv:2601.13519v4 Announce Type: replace-cross Abstract: This paper introduces a new problem-dependent regret measure for online convex optimization with smooth losses. The notion, which we call the...