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:2609. 01999v1 Announce Type: cross Abstract: We study a variant of the Thompson Sampling (TS) algorithm, called $\alpha$-TS, for solving stochastic generalized linear bandit problems.
By Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya, Bani K. Mallick
arXiv:2606. 15369v1 Announce Type: new Abstract: We study repeated bilateral trade from a fairness perspective.
By Fran\c{c}ois Bachoc, Roberto Colomboni, Emilie Kaufmann
arXiv:2607. 07304v1 Announce Type: new Abstract: In this paper we first study the problem of generalized linear bandit (GLB) under heavy-tailed noise.
By Tianshuo Zheng, Ting Wu, Zhi-Hua Zhou, Keqin Liu
arXiv:2607. 27073v1 Announce Type: new Abstract: We study online convex optimization (OCO) in non-stationary environments under heavy-tailed noise, where the stochastic gradient oracle admits only a finite $p$-th central moment for some $p \in (1, 2]$.
By Vaneet Aggarwal
We study repeated bilateral trade from a fairness perspective. At each round, a fresh seller-buyer pair arrives, and the platform posts a price before observing the traders' valuations.