21 citations · 49 across the 17 of their papers we have counts for
10 papers · 1 filter
Adaptively Perturbed Mirror Descent for Learning in Games
Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto +1
This paper proposes a payoff perturbation technique for the Mirror Descent (MD) algorithm in games where the gradient of the payoff functions is monotone in the strategy profile sp…
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games
Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto +2
This paper proposes Mutation-Driven Multiplicative Weights Update (M2WU) for learning an equilibrium in two-player zero-sum normal-form games and proves that it exhibits the last-i…
Mutation-Driven Follow the Regularized Leader for Last-Iterate Convergence in Zero-Sum Games
Kenshi Abe, Mitsuki Sakamoto, Atsushi Iwasaki
In this study, we consider a variant of the Follow the Regularized Leader (FTRL) dynamics in two-player zero-sum games. FTRL is guaranteed to converge to a Nash equilibrium when ti…
Evaluating the Efficiency of Regulation in Matching Markets with Distributional Disparities
Kei Ikegami, Atsushi Iwasaki, Akira Matsushita +1
Cap-based regulations are widely used to address distributional disparities in matching markets, but their efficiency relative to alternative instruments such as subsidies remains…
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search
Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki
This paper considers the capacity expansion problem in two-sided matchings, where the policymaker is allowed to allocate some extra seats as well as the standard seats. In medical…
Approximately Stable Matchings with General Constraints
Yasushi Kawase, Atsushi Iwasaki
This paper focuses on two-sided matching where one side (a hospital or firm) is matched to the other side (a doctor or worker) so as to maximize a cardinal objective under general…