8 citations · 8 across the 4 of their papers we have counts for
5 papers
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…
Instance-wise Supervision-level Optimization in Active Learning
Shinnosuke Matsuo, Riku Togashi, Ryoma Bise +2
Active learning (AL) is a label-efficient machine learning paradigm that focuses on selectively annotating high-value instances to maximize learning efficiency. Its effectiveness c…
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
Tatsuhiro Shimizu, Koichi Tanaka, Ren Kishimoto +3
We explore off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits (CCB), where a policy selects a subset in the action space. For example, it might choose a…
Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It
Yuta Saito, Masahiro Nomura
There has been a growing interest in off-policy evaluation in the literature such as recommender systems and personalized medicine. We have so far seen significant progress in deve…
Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction
Haruka Kiyohara, Masahiro Nomura, Yuta Saito
We study off-policy evaluation (OPE) in the problem of slate contextual bandits where a policy selects multi-dimensional actions known as slates. This problem is widespread in reco…