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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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About Me
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Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
japanese
publications
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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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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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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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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