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
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
arXiv:2503. 01701v2 Announce Type: replace-cross Abstract: Most microeconomic models of interest involve optimizing a piecewise linear function.
By Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
arXiv:2605. 21107v2 Announce Type: replace Abstract: We study constrained online convex optimization with adversarial time-varying constraints.
By Dhruv Sarkar, Abhishek Sinha
arXiv:2611.05580v1 Announce Type: cross
Abstract: We study repeated general games over $T$ rounds between a utility-maximizing optimizer and a learning agent, where budget constraints couple the play...
By Giannis Fikioris, Balasubramanian Sivan, \'Eva Tardos
arXiv:2608. 06825v1 Announce Type: new Abstract: Learning from correct demonstrations is harder than supervised learning when many answers are correct: after predicting, the learner sees one valid answer but not whether its own answer was valid, nor any reward.
By Pahan Dewasurendra
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