← Back to all news
arXiv Machine Learning October 15, 2026 By Angelos Assos, Anand Brahmbhatt, Elad Hazan

Tracking a Small Set of Experts in Online Convex Optimization

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

One email a morning, machine-written

One email a day, machine-written, one click to leave. We never share your address.

Related stories

arXiv Machine Learning
Jul 15

A Geometric Approach to Constrained Online Learning

arXiv:2605. 21107v2 Announce Type: replace Abstract: We study constrained online convex optimization with adversarial time-varying constraints.

By Dhruv Sarkar, Abhishek Sinha
safety
More like this →
arXiv Statistics ML
Oct 26

Strongly Adaptive Online Learning with Time-Varying Movement Costs

arXiv:2610.32740v1 Announce Type: cross Abstract: We study strongly adaptive regret for Online Convex Optimization (OCO) with time-varying movement costs, a setting that evaluates a learner's perform...

By Andrew Jacobsen, Emmanuel Esposito, Hao Qiu, Mengxiao Zhang
More like this →
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.

By Shinsaku Sakaue
More like this →
arXiv Statistics ML
Oct 1

Lower Bounds for Linear-Oracle Online Learning

arXiv:2609.38375v1 Announce Type: new Abstract: Can a constant number of linear minimizations per round improve on the $T^{3/4}$ regret rate of online Frank-Wolfe on general convex sets? Weibel et al...

By Mohit Sinha
More like this →
arXiv Machine Learning
Aug 27

Minimax Alternating Regret for the Experts Problem and Online Convex Optimization

arXiv:2608. 25182v1 Announce Type: cross Abstract: In this paper, we study alternating regret in online convex optimization (OCO), motivated by the success of alternating learning dynamics in two-player games.

By Mengxiao Zhang
More like this →
Hugging Face Trending Papers
Oct 6

Optimal and Efficient Online Inverse Optimization

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...

More like this →
About Pricing API Newsletter Sources Privacy Terms Refunds Accessibility Provider info Contact RSS

The Flow links to publishers and never republishes their articles. Summaries are machine-generated.

v1.1.0 · 5f852ea