Tatsuhiro Shimizu

Tatsuhiro Shimizu

he/him

Interested in machine learning and causal inference

Publications

You can also find my articles on my Google Scholar profile.

Conference Papers

Off-Policy Evaluation and Learning for the Future under Non-Stationarity

Published in Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025

We study the novel problem of future off-policy evaluation (F-OPE) and learning (F-OPL) for estimating and optimizing the future value of policies in non-stationary environments, where distributions vary over time.

Recommended citation: Shimizu, Tatsuhiro, et al. "Off-Policy Evaluation and Learning for the Future under Non-Stationarity." Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1. 2025.
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Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits

Published in Proceedings of the 18th ACM Conference on Recommender Systems, 2024

We explore off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits (CCB).

Recommended citation: Shimizu, Tatsuhiro, Koichi Tanaka, Ren Kishimoto, Haruka Kiyohara, Masahiro Nomura, and Yuta Saito. "Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits." In Proceedings of the 18th ACM Conference on Recommender Systems, pp. 733-741. 2024.
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Doubly Robust Estimator for Off-Policy Evaluation with Large Action Spaces

Published in 2023 IEEE Symposium Series On Computational Intelligence, 2023

We study Off-Policy Evaluation (OPE) in contextual bandit settings with large action spaces.

Recommended citation: Shimizu, Tatsuhiro and Forastiere, Laura. (2023). "Doubly Robust Estimator for Off-Policy Evaluation with Large Action Spaces." in Proceedings of 2023 IEEE Symposium Series On Computational Intelligence.
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Diffusion Model in Causal Inference with Unmeasured Confounders

Published in 2023 IEEE Symposium Series On Computational Intelligence, 2023

We study how to extend the use of the diffusion model to answer the causal question from the observational data under the existence of unmeasured confounders.

Recommended citation: Shimizu, Tatsuhiro. (2023). "Diffusion Model in Causal Inference with Unmeasured Confounders." in Proceedings of 2023 IEEE Symposium Series On Computational Intelligence.
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