1 citations · 1 across the 3 of their papers we have counts for
6 papers
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…
Abelian Neural Networks
Kenshin Abe, Takanori Maehara, Issei Sato
We study the problem of modeling a binary operation that satisfies some algebraic requirements. We first construct a neural network architecture for Abelian group operations and de…
A Practical Guide of Off-Policy Evaluation for Bandit Problems
Masahiro Kato, Kenshi Abe, Kaito Ariu +1
Off-policy evaluation (OPE) is the problem of estimating the value of a target policy from samples obtained via different policies. Recently, applying OPE methods for bandit proble…
Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games
Kenshi Abe, Yusuke Kaneko
Off-policy evaluation (OPE) is the problem of evaluating new policies using historical data obtained from a different policy. In the recent OPE context, most studies have focused o…
A Simple Heuristic for Bayesian Optimization with A Low Budget
Masahiro Nomura, Kenshi Abe
The aim of black-box optimization is to optimize an objective function within the constraints of a given evaluation budget. In this problem, it is generally assumed that the comput…
Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero
Kenshin Abe, Zijian Xu, Issei Sato +1
There have been increasing challenges to solve combinatorial optimization problems by machine learning. Khalil et al. proposed an end-to-end reinforcement learning framework, S2V-D…