Ren Kishimoto — 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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2024

Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation

Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, Yuta Saito

ICLR 2024poster

**Off-Policy Evaluation (OPE)** aims to assess the effectiveness of counterfactual policies using offline logged data and is frequently utilized to identify the top-$k$ promising policies for deployment in online A/B tests. Existing evaluation metrics for OPE estimators primarily focus on the "accur…

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