ジャーナル論文 / Journal
  1. Yara, A., and Terada , Y., "Nonparametric logistic regression with deep learning", Bernoulli 32(2), 952–977, (2026).
  2. Jiang, Y., and Shimizu, S., "Financial literacy may not directly drive investment participation or retirement planning in Japan", Frontiers in Behavioral Economics 4, 1725333, (2026).
  3. Belangoy, K. P., Nishimura, Y., Harada, K., Hagiya, H., Vu, Q. T., Ouddoud, H., Lescano, J. I., Yamamoto, M., Takeda, T., Hamano, H., Koyama, T., and Zamami, Y., "Global trends in systemic sclerosis-related mortality, 2001–2023: an epidemiological analysis using World Health Organization mortality data", Clin. Rheumatol., 1–8, (2026).
  4. 高山正行, 清水昌平, "科学技術・イノベーション政策の立案・評価への統計的因果探索の応用", 研究 技術 計画 40(3-4), 341–355, (2025).
  5. 瀬戸ひろえ, 前川眞一, 山本倫生, "二値分類器の公平性の評価 —安定性に着目した評価指標の開発とアルゴリズムの比較検討—", データ分析の理論と応用 14(1), 1–14, (2025).
  6. Yamagiwa, H., Hashimoto, R., Arakane, K., Murakami, K., Soeda, S., Oyama, M., Zhu, Y., Okada, M., and Shimodaira, H., "Predicting drug–gene relations via analogy tasks with word embeddings", Sci. Rep. 15(1), 17240, (2025).
  7. Watamura, E., Yamamoto, M., Mukai, T., Matsuki, Y., Yuyama, Y., and Sadamura, M., "Punitive Penalties for the Maltreatment of Animals: A Case Study of People’s Perceptions", Anthrozoös 38(3), 527–543, (2025).
  8. Watamura, E., Ioku, T., and Yamamoto, M., "Sliding or securing? Emotional ambivalence and public perception after euthanasia legalization in Japan", Death Stud. ahead-of-print(ahead-of-print), 1–8, (2025).
  9. Tsubota, Y., and Yamamoto, M., "Parametric causal mediation analysis with asymmetric binary regression model", Behaviormetrika, 1–36, (2025).
  10. Terada, Y., and Guan, X., "A note on the k-means clustering for missing data", Transactions on Machine Learning Research, (2025).
  11. Takayama, M., Okuda, T., Pham, T., Ikenoue, T., Fukuma, S., Shimizu, S., and Sannai, A., "Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach.", Trans. Mach. Learn. Res. 2025, (2025).
  12. Shinozaki, M., Hishida, H., Gondo, Y., Yamamoto, M., Suzuki, T., Miura, R., Sakurai, T., Takeda, A., and Arahata, Y., "Machine learning model for predicting the conversion to dementia using the Cube Copying Test", Journal of Alzheimer’s Disease 108(1_suppl), s141–s158, (2025).
  13. Seto, H., Kitora, S., Oyama, A., Toki, H., Yamamoto, R., and Yamamoto, M., "Variable-based probabilistic calibration with binary outcome", Biostatistics 26(1), kxaf026, (2025).
  14. Okuno, A., and Sasaki, M., "A Systematic Approach to Decomposing Numerical Turbulence Fields into Substructures", Phys. Plasmas 32(3), 032502, (2025).
  15. Okuno, A., and Hattori, K., "A greedy and optimistic clustering for leveraging individual covariate uncertainty", Ann. Inst. Statist. Math., 1–22, (2025).
  16. Morinishi, Y., and Shimizu, S., "Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding.", Trans. Mach. Learn. Res. 2025, (2025).
  17. Morikawa, K., Terada, Y., and Kim, J. K., "Semiparametric adaptive estimation under informative sampling", Ann. Statist. 53(3), 1347–1369, (2025).
  18. Hagiya, H., Harada, K., Nishimura, Y., Yamamoto, M., Nishimura, S., Yamamoto, M., Niimura, T., Osaki, Y., Vu, Q. T., Fujii, M., Sako, N., Takeda, T., Hamano, H., Zamami, Y., and Koyama, T., "Global trends in mortality related to pulmonary embolism: an epidemiological analysis of data from the World Health Organization mortality database from 2001 to 2023", EClinicalMedicine 86, 103389, (2025).
  19. Zheng, Y., Huang, B., Chen, W., Ramsey, J., Gong, M., Cai, R., Shimizu, S., Spirtes, P., and Zhang, K., "Causal-learn: Causal Discovery in Python", J. Mach. Learn. Res. 25, 1–7, (2024).
  20. Okuno, A., and Imaizumi, M., "Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression", Electron. J. Stat., (2024).
  21. Okuno, A., and Harada, K., "An interpretable neural network-based non-proportional odds model for ordinal regression", J. Comput. Graph. Stat. 33(4), 1454–1463, (2024).
  22. Okuno, A., Morishita, Y., and Mototake, Y., "Autoregressive with Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series", Access 12, 24621–24630, (2024).
  23. Okuno, A., "Minimizing robust density power-based divergences for general parametric density models", Ann. Inst. Statist. Math. 76(5), 851–875, (2024).
  24. OKUNO, A., KODAHARA, T., and SASAKI, M., "Hierarchical Clustering of Modes in Numerical Turbulence Fields", Plasma and Fusion Research: Rapid Communications 19(0), 1201035, (2024).
  25. Maeda, T. N., and Shimizu, S., "Use of prior knowledge to discover causal additive models with unobserved variables and its application to time series data", Behaviormetrika, 1–19, (2024).
  26. Koyama, N., Sakai, Y., Sasaoka, S., Dominguez, D., Somiya, K., Omae, Y., Terada, Y., Meyer-Conde, M., and Takahashi, H., "Enhancing the rationale of convolutional neural networks for glitch classification in gravitational wave detectors: a visual explanation", Mach. Learn.: Sci. Technol. 5(3), 035028, (2024).
  27. Zhou, X., Zheng, X., Shu, T., Liang, W., Wang, K. I., Qi, L., Shimizu, S., and Jin, Q., "Information Theoretic Learning-Enhanced Dual-Generative Adversarial Networks With Causal Representation for Robust OOD Generalization", IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1–14, (2023).
  28. Zhou, X., Zheng, X., Cui, X., Shi, J., Liang, W., Yan, Z., Yang, L. T., Shimizu, S., and Wang, K. I., "Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networks", IEEE J. Select. Areas Commun. 41(10), 3191–3211, (2023).
  29. Watamura, E., Ioku, T., Mukai, T., and Yamamoto, M., "Empathetic Robot Judge, we Trust You", International Journal of Human-Computer Interaction ahead-of-print(ahead-of-print), 1–10, (2023).
  30. Suzuki, E., Yamamoto, M., and Yamamoto, E., "Exchangeability of Measures of Association Before and After Exposure Status Is Flipped: Its Relationship With Confounding in the Counterfactual Model", J. Epidemiol. 33(8), 385–389, (2023).
  31. Okuno, A., and Yano, K., "A Generalization Gap Estimation for Overparameterized Models via the Langevin Functional Variance", J. Comput. Graph. Stat. 32(4), 1287–1295, (2023).
  32. Koyama, T., Iinuma, S., Yamamoto, M., Niimura, T., Osaki, Y., Nishimura, S., Harada, K., Zamami, Y., and Hagiya, H., "International Trends in Adverse Drug Event-Related Mortality from 2001 to 2019: An Analysis of the World Health Organization Mortality Database from 54 Countries", Drug Saf., 1–13, (2023).
  33. Hagiya, H., Osaki, Y., Yamamoto, M., Niimura, T., Harada, K., Higashionna, T., Hamano, H., Zamami, Y., Hinotsu, S., and Koyama, T., "Global trends of seasonal influenza-associated mortality in 2001–2018: A longitudinal epidemiological study", J. Infect. 87(3), e54–e57, (2023).
  34. Guan, X., and Terada, Y., "Sparse kernel k-means for high-dimensional data", Pattern Recognit. 144, 109873, (2023).
  35. Zhou, X., Xu, X., Liang, W., Zeng, Z., Shimizu, S., Yang, L. T., and Jin, Q., "Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems.", IEEE Trans. Ind. Informatics 18(2), 1377–1386, (2022).
  36. Zhou, X., Liang, W., Li, W., Yan, K., Shimizu, S., and Wang, K. I., "Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System.", IEEE Internet Things J. 9(12), 9310–9319, (2022).
  37. Suzuki, K., Abe, M. S., Kumakura , D., Nakaoka, S., Fujiwara , F., Miyamoto , H., Nakaguma , T., Okada , M., Sakurai , K., Shimizu , S., Iwata , H., Masuya, H., Nihei, N., and Ichihashi, Y., "Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks", Int. J. Environ. Res. Public. Health 19(3), (2022).
  38. Suzuki, E., Yamamoto, M., and Yamamoto, E., "A general explanation of the counterfactual definition of confounding", J. Clin. Epidemiol. 148, 189–192, (2022).
  39. Maeda, T. N., and Shimizu, S., "Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders.", Int. J. Data Sci. Anal. 13(2), 77–89, (2022).
  40. Akamatsu, S., Terada, N., Takata, R., Kinoshita, H., Shimatani, K., Momozawa, Y., Yamamoto, M., Tada, H., Kawamorita, N., Narita, S., Kato, T., Nitta, M., Kandori, S., Koike, Y., Inazawa, J., Kimura, T., Kimura, H., Kojima, T., Terachi, T., Sugimoto, M., Habuchi, T., Arai, Y., Yamamoto, S., Matsuda, T., Obara, W., Kamoto, T., Inoue, T., Nakagawa, H., Ogawa, O., and group, o. b., "Clinical Utility of Germline Genetic Testing in Japanese Men Undergoing Prostate Biopsy", JNCI Cancer Spectrum 6(1), pkac001, (2022).
  41. Zeng, Y., Hao, Z., Cai, R., Xie, F., Huang, L., and Shimizu, S., "Nonlinear Causal Discovery for High-Dimensional Deterministic Data", IEEE Trans. Neural Netw. Learn. Syst., (2021).
  42. Maeda, T., and Shimizu, S., "Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders", International Journal of Data Science and Analytics, (2021).
国際会議 / Proceedings
  1. Yokoyama, H., Shingaki, R., Nishino, K., Shimizu, S., and Pham, T., "Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis", 2025 International Joint Conference on Neural Networks (IJCNN) 00, 1–10, (2025).
  2. Oyama, M., Yamagiwa, H., Takase, Y., and Shimodaira, H., "Mapping 1,000+ Language Models via the Log-Likelihood Vector", Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 32983–33038, (2025).
  3. Oyama, M., Kishino, R., Yamagiwa, H., and Shimodaira, H., "Likelihood Variance as Text Importance for Resampling Texts to Map Language Models", Findings of the Association for Computational Linguistics: EMNLP 2025, 9453–9465, (2025).
  4. Kishino, R., Yamagiwa, H., Nagata, R., Yokoi, S., and Shimodaira, H., "Quantifying Lexical Semantic Shift via Unbalanced Optimal Transport", Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 15913–15933, (2025).
  5. Yokoi, S., Bao, H., Kurita, H., and Shimodaira, H., "Zipfian Whitening", 38th Conference on Neural Information Processing Systems (NeurIPS 2024), (2024).
  6. Takahashi, D., Shimizu, S., and Tanaka, T., "Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating", 2024 International Joint Conference on Neural Networks (IJCNN) 00, 1–8, (2024).
  7. Pham, T., Shimizu, S., Hino, H., and Le, T., "Scalable counterfactual distribution estimation in multivariate causal models", Proceedings of the Third Conference on Causal Learning and Reasoning, PMLR 236, 1118–1140, (2024).
  8. Ishibashi, Y., Yokoi, S., Sudoh, K., and Nakamura, S., "Subspace Representations for Soft Set Operations and Sentence Similarities", Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 3512–3524, (2024).
  9. Inoshita, K., Zhou, X., and Shimizu, S., "Multi-Domain and Multi-View Oriented Deep Neural Network for Sentiment Analysis in Large Language Models", 2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics 00, 149–156, (2024).
  10. Yamagiwa, H., Yokoi, S., and Shimodaira, H., "Improving word mover’s distance by leveraging self-attention matrix", Findings of the Association for Computational Linguistics: EMNLP 2023, 11160–11183, (2023).
  11. Yamagiwa, H., Oyama, M., and Shimodaira, H., "Discovering Universal Geometry in Embeddings with ICA", Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 4647–4675, (2023).
  12. Wani, S., Zhou, X., and Shimizu, S., "BiLSTM and VAE Enhanced Multi-Task Neural Network for Trust-Aware E-Commerce Product Analysis", 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) 00, 780–787, (2023).
  13. Tsuchiya, T., Ito, S., and Honda, J., "Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds", Advances in Neural Information Processing Systems 36, 47406–47437, (2023).
  14. Tsuchiya, T., Ito, S., and Honda, J., "Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits", Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS2023), Proceedings of Machine Learning Research 206, 8117–8144, (2023).
  15. Oyama, M., Yokoi, S., and Shimodaira, H., "Norm of Word Embedding Encodes Information Gain", Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2108–2130, (2023).
  16. Lee, J., Honda, J., Chiang, C. K., and Sugiyama, M., "Optimality of Thompson sampling with noninformative priors for Pareto bandits", Proceedings of 40th International Conference on Machine Learning (ICML2023), Proceedings of Machine Learning Research 202, 18810–18851, (2023).
  17. Kurita, H., Kobayashi, G., Yokoi, S., and Inui, K., "Contrastive Learning-based Sentence Encoders Implicitly Weight Informative Words", In Findings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP2023 Findings), (2023).
  18. Kobayashi, G., Kuribayashi, T., Yokoi, S., and Inui, K., "Transformer Language Models Handle Word Frequency in Prediction Head", In Findings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL2023 Findings), 4523–4535, (2023).
  19. Kikuchi, G., and Shimizu, S., "Structure Learning for Groups of Variables in Nonlinear Time-Series Data with Location-Scale Noise", Proceedings of the 2023 Causal Analysis Workshop Series, PMLR 223, 20–39, (2023).
  20. Jiang, Y., and Shimizu, S., "Linkages among the Foreign Exchange, Stock, and Bond Markets in Japan and the United States", Proceedings of the 2023 Causal Analysis Workshop Series, PMLR 223, 1–19, (2023).
  21. Arase, Y., Bao, H., and Yokoi, S., "Unbalanced Optimal Transport for Unbalanced Word Alignment", Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL2023) 1, 3966–3986, (2023).
  22. Zeng, Y., Shimizu, S., Matsui, H., and Sun, F., "Causal Discovery for Linear Mixed Data.", CLeaR, 994–1009, (2022).
  23. Uemura, K., Takagi, T., Takayuki, K., Yoshida, H., and Shimizu, S., "A Multivariate Causal Discovery based on Post-Nonlinear Model.", Proceedings of the First Conference on Causal Learning and Reasoning, PMLR 177, 826–839, (2022).
  24. Zeng, Y., Shimizu, S., Cai, R., Xie, F., Yamamoto, M., and Hao, Z., "Causal Discovery with Multi-Domain LiNGAM for Latent Factors", Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), (2021).
  25. Maeda, T., and Shimizu, S., "Causal Additive Models with Unobserved Variables", Proceedings of Machine Learning Research, (2021).
  26. Kiritosh, K., Izumitani, T., Koyama, K., Okawachi, T., Asahara, K., and Shimizu, S., "Estimating individual-level optimal causal interventions combining causal models and machine learning models", Proceedings of The KDD'21 Workshop on Causal Discovery, (2021).
レビュー / Review
  1. 田栗正隆, 高橋邦彦, 小向翔, 伊藤ゆり, 服部聡, 船渡川伊久子, 篠崎智大, 山本倫生, 林賢一, "疫学分野での計量生物学の発展", 計量生物学 44(2), 129–200, (2024).
  2. Kuroki, Y., Honda, J., and Sugiyama, M., "Combinatorial pure exploration with full-bandit feedback and beyond: Solving combinatorial optimization under uncertainty with limited observation", The Fields Institute Communications Series on Data Science and Optimization, (2023).