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Toshiharu Sugawara

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#machine learning Preprint Sep 2026

Recommendation Ranking Off-Policy Evaluation under Ranking-Dependent Examination via Examination-Relevance Decomposition

Two estimators based on the decomposition of clicks into examination and relevance are proposed, one of which corrects the IIPS bias using policy examination probability ratios and extends LE-IIPS to a doubly robust framework.

Riki Okamura, Toshiharu Sugawara · 0 citations

Conference Jul 2026

Language-Grounded Strategy-Following Multi-Agent Deep Reinforcement Learning for Controllability of Real-World Applications

We present lg-sfDA6-X, a language-grounded strategy-following distributed attentional actor architecture after conditional attention, for multi-agent deep reinforcement learning (MADRL). The proposed architecture aims to enable controllable and coordinated agent behaviors in application systems by leveraging a shared s...

Yoshinari Motokawa, Toshiharu Sugawara · Annual International Compute... · 0 citations