3 papers
cs.LG2026
Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching
Ren Kishimoto, Rikiya Takehi, Koichi Tanaka +4
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 a…
cs.LG2026
Offline Contextual Bandits in the Presence of New Actions
Ren Kishimoto, Tatsuhiro Shimizu, Kazuki Kawamura +6
Automated decision-making algorithms drive applications such as recommendation systems and search engines. These algorithms often rely on off-policy contextual bandits or off-polic…
cs.LG2026
Off-Policy Learning with Limited Supply
Koichi Tanaka, Ren Kishimoto, Bushun Kawagishi +4
We study off-policy learning (OPL) in contextual bandits, which plays a key role in a wide range of real-world applications such as recommendation systems and online advertising. T…