ジャーナル論文 / Journal
  1. Okuno, A., and Sasaki, M., "A Systematic Approach to Decomposing Numerical Turbulence Fields into Substructures", Phys. Plasmas 32(3), 032502, (2025).
  2. 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).
  3. Okuno, A., and Imaizumi, M., "Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression", Electron. J. Stat., (2024).
  4. 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).
  5. 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).
  6. Okuno, A., "Minimizing robust density power-based divergences for general parametric density models", Ann. Inst. Statist. Math. 76(5), 851–875, (2024).
  7. 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).
  8. 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).
  9. 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).
  10. 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).
  11. 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).
  12. 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).
  13. Guan, X., and Terada, Y., "Sparse kernel k-means for high-dimensional data", Pattern Recognit. 144, 109873, (2023).
  14. 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).
  15. 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).
  16. 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).
  17. 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).
  18. 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).
  19. 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).
  20. 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. Yokoi, S., Bao, H., Kurita, H., and Shimodaira, H., "Zipfian Whitening", 38th Conference on Neural Information Processing Systems (NeurIPS 2024), (2024).
  2. 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).
  3. 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).
  4. 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).
  5. 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).
  6. 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).
  7. 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).
  8. 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).
  9. 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).
  10. 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).
  11. 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).
  12. 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).
  13. 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).
  14. 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).
  15. Zeng, Y., Shimizu, S., Matsui, H., and Sun, F., "Causal Discovery for Linear Mixed Data.", CLeaR, 994–1009, (2022).
  16. 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).
  17. 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).
  18. Maeda, T., and Shimizu, S., "Causal Additive Models with Unobserved Variables", Proceedings of Machine Learning Research, (2021).
  19. 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).