The paper extends dueling bandits to episodic Markov decision processes where selecting a pair of arms not only yields a noisy preference but also influences the next state. It defines a state‑wise preference‑based MDP with adversarially changing preferences and Borda‑score losses, and proves a minimax regret of ∫Θ(min{HT,(H^3SK)^{1/3}T^{2/3}}) under known transitions. The authors provide an online linear‑optimization algorithm achieving this bound, a policy‑optimization algorithm with closed‑form updates, and extensions to unknown transitions.
By Taira Tsuchiya, Shinji Ito, Haipeng Luo
arXiv:2605.12340v5 Announce Type: replace-cross
Abstract: Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. Real-world deployments require handling...
By Dang Hoang Duy, Yannis Montreuil, Maxime Meyer, Axel Carlier, Lai Xing Ng, Wei Tsang Ooi
arXiv:2611.08173v1 Announce Type: cross
Abstract: Bilateral trade models the task of intermediating between two strategic agents, a seller and a buyer, willing to trade a good for which they hold pri...
By Johannes Br\"ustle, Simone Di Gregorio, Federico Fusco, Stefano Leonardi
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
By Panayotis Mertikopoulos
arXiv:2603. 25029v4 Announce Type: replace Abstract: We study online convex optimization (OCO) with two-point bandit feedback against a non-anticipating adaptive adversary.
By Haishan Ye
arXiv:2610.09691v1 Announce Type: cross
Abstract: We study online learning in market making, where, at each round, a market maker posts bid and ask prices before observing the market price and the pr...
By Maria Elena Vischi, Francesco Emanuele Stradi, Alberto Marchesi