Rikiya Takehi — accepted AI-conference papers

2026

Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching

Ren Kishimoto, Rikiya Takehi, Koichi Tanaka, Yoji Tomita, Masahiro Nomura, Riku Togashi +1

ICLR 2026poster

On two-sided matching platforms such as online dating and recruiting, recommendation algorithms often aim to maximize the total number of matches. However, this objective creates an imbalance, where some users receive far too many matches while many others receive very few and eventually abandon the…

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2025

A General Framework for Off-Policy Learning with Partially-Observed Reward

Rikiya Takehi, Masahiro Asami, Kosuke Kawakami, Yuta Saito

ICLR 2025poster

Off-policy learning (OPL) in contextual bandits aims to learn a decision-making policy that maximizes the target rewards by using only historical interaction data collected under previously developed policies. Unfortunately, when rewards are only partially observed, the effectiveness of OPL degrades…

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