arXiv:2511.05620v2 Announce Type: replace
Abstract: We study worst-case dynamic regret of specific multi-armed bandit algorithms on piecewise-stationary instances with at most one breakpoint. Our con...
By Gal Mendelson, Eyal Tadmor
The paper studies contextual bilateral trade with full feedback, showing that action-independent observations eliminate the usual polynomial adaptation penalty seen in heavy-tailed bandits. It presents fully parameter-free algorithms that achieve oracle minimax regret rates without knowing the moment order or scale, and derives new regret bounds for both parametric and nonparametric settings. The key technical insight is a paired squared‑loss statistic whose noise cancels, enabling model selection and yielding regret rates that interpolate between classical nonparametric and linear extremes.
By Hangyi Zhao
arXiv:2610.01951v1 Announce Type: cross
Abstract: Top-two algorithms are simple and effective for fixed-confidence best-arm identification, but their sharp non-asymptotic behavior is still not well u...
By Nam Nguyen, Tuan Quang Dam
arXiv:2604. 20024v2 Announce Type: replace Abstract: We study replicable algorithms for stochastic multi-armed bandits (MAB) and linear bandits with UCB (Upper Confidence Bound) based exploration.
By Rohan Deb, Udaya Ghai, Karan Singh, Arindam Banerjee
arXiv:2607. 02196v1 Announce Type: new Abstract: We study online resource allocation when both rewards and consumption sizes may be continuously distributed.
By Jiawei Zhang
arXiv:2606. 08028v1 Announce Type: new Abstract: We study high-probability regret bounds for online convex optimization (OCO) with strongly convex losses and establish three results that resolve open questions at the intersection of noise adaptivity, feedback structure, and constraint satisfaction.
By Wentao Zhang, Yutong Zhang, Wentao Mo