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stat.ML2024
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…
stat.ML2024★ 8 cited
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…