7 citations · 19 across the 31 of their papers we have counts for
5 papers · 1 filter
Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback
Shinji Ito, Kevin Jamieson, Haipeng Luo +2
We study online learning in finite-horizon episodic Markov decision processes (MDPs) under the challenging aggregate bandit feedback model, where the learner observes only the cumu…
From Dueling Bandits to Episodic MDPs: Learning with Adversarial Preference Feedback
Taira Tsuchiya, Shinji Ito, Haipeng Luo
In dueling bandits, the learner compares two arms in each round and observes only which one is preferred. This paper asks what changes when the compared pair also affects the next…
Optimal Regret of Bernoulli Bandits under Global Differential Privacy
Achraf Azize, Yulian Wu, Junya Honda +3
As sequential learning algorithms are increasingly applied to real life, ensuring data privacy while maintaining their utilities emerges as a timely question. In this context, regr…
Bandit Max-Min Fair Allocation
Tsubasa Harada, Shinji Ito, Hanna Sumita
In this paper, we study a new decision-making problem called the bandit max-min fair allocation (BMMFA) problem. The goal of this problem is to maximize the minimum utility among a…
Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback
Shinji Ito, Haipeng Luo, Taira Tsuchiya +1
No-regret self-play learning dynamics have become one of the premier ways to solve large-scale games in practice. Accelerating their convergence via improving the regret of the pla…