Koichi Tanaka — 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…

Cited by 0 Source ↗Scholar ↗

2026

Off-Policy Evaluation for Ranking Policies under Deterministic Logging Policies

Koichi Tanaka, Kazuki Kawamura, Takanori Muroi, Yusuke Narita, Yuki Sasamoto, Kei Tateno +3

ICLR 2026poster

Off-Policy Evaluation (OPE) is an important practical problem in algorithmic ranking systems, where the goal is to estimate the expected performance of a new ranking policy using only offline logged data collected under a different, logging policy. Existing estimators, such as the ranking-wise and p…

Cited by 0 Source ↗Scholar ↗