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Causal models and prediction in cell line perturbation experiments.
James P. Long, Yumeng Yang, Shohei Shimizu, Thong Pham, Kim-Anh Do
BMC Bioinform. Vol. 26 No. 1 p. 4-4 2025/12 Research paper (scientific journal)
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Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Data.
Takashi Nicholas Maeda, Shohei Shimizu, Hidetoshi Matsui
CoRR Vol. abs/2505.08371 2025/05 Research paper (scientific journal)
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Causal Additive Models with Unobserved Causal Paths and Backdoor Paths.
Thong Pham, Takashi Nicholas Maeda, Shohei Shimizu
CoRR Vol. abs/2502.07646 2025/02 Research paper (scientific journal)
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Novel MITM attack scheme based on built-in negotiation for blockchain-based digital twins.
Xin Liu 0050, Rui Zhou 0005, Shohei Shimizu, Rui Chong, Qingguo Zhou, Xiaokang Zhou
Digit. Commun. Networks Vol. 11 No. 1 p. 256-267 2025 Research paper (scientific journal)
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Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach.
Masayuki Takayama, Tadahisa Okuda, Thong Pham, Tatsuyoshi Ikenoue, Shingo Fukuma, Shohei Shimizu, Akiyoshi Sannai
Trans. Mach. Learn. Res. Vol. 2025 2025 Research paper (scientific journal)
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Multi-Domain and Multi-View Oriented Deep Neural Network for Sentiment Analysis in Large Language Models
Keito Inoshita, Xiaokang Zhou, Shohei Shimizu
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 p. 149-156 2024/08/19 Research paper (international conference proceedings)
Publisher: IEEE
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Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data.
Takashi Nicholas Maeda, Shohei Shimizu
Behaviormetrika 2024/08 Research paper (scientific journal)
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Does Financial Literacy Impact Investment Participation and Retirement Planning in Japan?
Yi Jiang, Shohei Shimizu
2024/05/02
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Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis.
Hiroshi Yokoyama, Ryusei Shingaki, Kaneharu Nishino, Shohei Shimizu, Thong Pham
CoRR Vol. abs/2411.06990 2024 Research paper (scientific journal)
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Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating.
Daisuke Takahashi, Shohei Shimizu, Takuma Tanaka
International Joint Conference on Neural Networks(IJCNN) p. 1-8 2024 Research paper (international conference proceedings)
Publisher: IEEE
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Causal-learn: Causal Discovery in Python.
Yujia Zheng 0001, Biwei Huang, Wei Chen 0103, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang 0001
Journal of Machine Learning Research Vol. 25 p. 60-8 2024 Research paper (scientific journal)
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Special issue: recent developments in causal inference and machine learning vol.2
Shohei Shimizu, Shuichi Kawano
Behaviormetrika Vol. 51 No. 1 p. 497-498 2024/01 Research paper (scientific journal)
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Scalable Counterfactual Distribution Estimation in Multivariate Causal Models.
Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le
CLeaR Vol. 236 p. 1118-1140 2024 Research paper (international conference proceedings)
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Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networks.
Xiaokang Zhou, Xuzhe Zheng, Xuesong Cui, Jiashuai Shi, Wei Liang 0006, Zheng Yan 0002, Laurence T. Yang, Shohei Shimizu, Kevin I-Kai Wang
IEEE Journal of Selected Areas in Communications Vol. 41 No. 10 p. 3191-3211 2023/10 Research paper (scientific journal)
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Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications.
Xiaokang Zhou, Xiaozhou Ye, Kevin I-Kai Wang, Wei Liang 0006, Nirmal-Kumar C. Nair, Shohei Shimizu, Zheng Yan 0002, Qun Jin
IEEE Transactions on Computational Social Systems Vol. 10 No. 4 p. 1742-1751 2023/08 Research paper (scientific journal)
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Nonlinear Causal Discovery for High-Dimensional Deterministic Data.
Yan Zeng 0002, Zhifeng Hao, Ruichu Cai, Feng Xie 0002, Libo Huang, Shohei Shimizu
IEEE Transactions on Neural Networks and Learning Systems Vol. 34 No. 5 p. 2234-2245 2023/05 Research paper (scientific journal)
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Information Theoretic Learning-Enhanced Dual-Generative Adversarial Networks With Causal Representation for Robust OOD Generalization
Xiaokang Zhou, Xuzhe Zheng, Tian Shu, Wei Liang, Kevin I.Kai Wang, Lianyong Qi, Shohei Shimizu, Qun Jin
IEEE Transactions on Neural Networks and Learning Systems 2023 Research paper (scientific journal)
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The KDD'23 Workshop on Causal Discovery, Prediction and Decision, 07 August 2023, Long Beach, CA, USA
CDPD Vol. 218 2023 Research paper (international conference proceedings)
Publisher: PMLR
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BiLSTM and VAE Enhanced Multi-Task Neural Network for Trust-Aware E-Commerce Product Analysis.
Shusuke Wani, Xiaokang Zhou, Shohei Shimizu
TrustCom p. 780-787 2023 Research paper (international conference proceedings)
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Preface: The 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision.
Thuc Duy Le, Jiuyong Li, Robert Ness, Sofia Triantafillou, Shohei Shimizu, Peng Cui 0001, Kun Kuang, Jian Pei, Fei Wang 0001, Mattia Prosperi
CDPD Vol. 218 p. 1-2 2023 Research paper (international conference proceedings)
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Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling.
Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu
CLeaR Vol. 213 p. 880-894 2023 Research paper (international conference proceedings)
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Structure Learning for Groups of Variables in Nonlinear Time-Series Data with Location-Scale Noise.
Genta Kikuchi, Shohei Shimizu
CAWS Vol. 223 p. 20-39 2023 Research paper (international conference proceedings)
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Linkages among the Foreign Exchange, Stock, and Bond Markets in Japan and the United States.
Yi Jiang, Shohei Shimizu
CAWS Vol. 223 p. 1-19 2023 Research paper (international conference proceedings)
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Prospects of Continual Causality for Industrial Applications.
Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu
AAAI Bridge Program Vol. 208 p. 18-24 2023 Research paper (international conference proceedings)
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Python package for causal discovery based on LiNGAM.
Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu
Journal of Machine Learning Research Vol. 24 p. 14-8 2023 Research paper (scientific journal)
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Special issue: Recent developments in causal inference and machine learning
Shohei Shimizu, Shuichi Kawano
Behaviormetrika Vol. 49 No. 2 p. 275-276 2022/07 Research paper (scientific journal)
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Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks
Kenta Suzuki, Masato S. Abe, Daiki Kumakura, Shinji Nakaoka, Fuki Fujiwara, Hirokuni Miyamoto, Teruno Nakaguma, Mashiro Okada, Kengo Sakurai, Shohei Shimizu, Hiroyoshi Iwata, Hiroshi Masuya, Naoto Nihei, Yasunori Ichihashi
International Journal of Environmental Research and Public Health Vol. 19 No. 3 2022/02 Research paper (scientific journal)
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CNN-GRU Based Deep Learning Model for Demand Forecast in Retail Industry.
Kazuhi Honjo, Xiaokang Zhou, Shohei Shimizu
International Joint Conference on Neural Networks(IJCNN) Vol. 2022-July p. 1-8 2022 Research paper (international conference proceedings)
Publisher: IEEE
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Causal Discovery for Linear Mixed Data.
Yan Zeng, Shohei Shimizu, Hidetoshi Matsui, Fuchun Sun
1st Conference on Causal Learning and Reasoning(CLeaR) Vol. 177 p. 994-1009 2022 Research paper (international conference proceedings)
Publisher: PMLR
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A Multivariate Causal Discovery based on Post-Nonlinear Model.
Kento Uemura, Takuya Takagi, Kambayashi Takayuki, Hiroyuki Yoshida, Shohei Shimizu
1st Conference on Causal Learning and Reasoning(CLeaR) Vol. 177 p. 826-839 2022 Research paper (international conference proceedings)
Publisher: PMLR
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Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems.
Xiaokang Zhou, Xuesong Xu, Wei Liang, Zhi Zeng, Shohei Shimizu, Laurence T. Yang, Qun Jin
IEEE Transactions on Industrial Informatics Vol. 18 No. 2 p. 1377-1386 2022 Research paper (scientific journal)
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Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System.
Xiaokang Zhou, Wei Liang, Weimin Li, Ke Yan, Shohei Shimizu, Kevin I-Kai Wang
IEEE Internet Things J. Vol. 9 No. 12 p. 9310-9319 2022 Research paper (scientific journal)
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Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders.
Takashi Nicholas Maeda, Shohei Shimizu
Int. J. Data Sci. Anal. Vol. 13 No. 2 p. 77-89 2022 Research paper (scientific journal)
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Estimating individual-level optimal causal interventions combining causal models and machine learning models.
Keisuke Kiritoshi, Tomonori Izumitani, Kazuki Koyama, Tomomi Okawachi, Keisuke Asahara, Shohei Shimizu
The KDD 2021 Workshop on Causal Discovery(CD@KDD) Vol. 150 p. 55-77 2021 Research paper (international conference proceedings)
Publisher: PMLR
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Causal Discovery with Multi-Domain LiNGAM for Latent Factors.
Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao
Causal Analysis Workshop Series(CAWS) Vol. 160 p. 1-4 2021 Research paper (conference, symposium, etc.)
Publisher: PMLR
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Discovery of Causal Additive Models in the Presence of Unobserved Variables.
Takashi Nicholas Maeda, Shohei Shimizu
CoRR Vol. abs/2106.02234 2021 Research paper (scientific journal)
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Causal additive models with unobserved variables.
Takashi Nicholas Maeda, Shohei Shimizu
UAI Vol. 161 p. 97-106 2021 Research paper (international conference proceedings)
Publisher: AUAI Press
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Causal Discovery with Multi-Domain LiNGAM for Latent Factors.
Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence(IJCAI) Vol. abs/2009.09176 p. 2097-2103 2021 Research paper (international conference proceedings)
Publisher: ijcai.org
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Intelligent Small Object Detection Based on Digital Twinning for Smart Manufacturing in Industrial CPS
Xiaokang Zhou, Xuesong Xu, Wei Liang, Zhi Zeng, Shohei Shimizu, Laurence T. Yang, Qun Jin
IEEE Transactions on Industrial Informatics p. 1-1 2021 Research paper (scientific journal)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
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Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems
Xiaokang Zhou, Wei Liang, Shohei Shimizu, Jianhua Ma, Qun Jin
IEEE Transactions on Industrial Informatics Vol. 17 No. 8 p. 5790-5798 2020/12/31 Research paper (scientific journal)
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Estimation of post-nonlinear causal models using autoencoding structure
K. Uemura, S. Shimizu
Proc. 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP2020) Vol. 2020-May p. 3312-3316 2020/05 Research paper (international conference proceedings)
Publisher: IEEE
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RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
T. N. Maeda, S. Shimizu
JMLR Workshop and Conference Proceedings, AISTATS2020 (Proc. 23rd International Conference on Artificial Intelligence and Statistics) Vol. 108 p. 735-745 2020/05 Research paper (scientific journal)
Publisher: PMLR
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B4SDC: A Blockchain System for Security Data Collection in MANETs,
Gao Liu, Huidong Dong, Zheng Yan, Xiaokang Zhou, Shohei Shimizu
IEEE Transactions on Big Data Vol. 8 No. 3 p. 739-752 2020/03 Research paper (scientific journal)
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Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders.
Takashi Nicholas Maeda, Shohei Shimizu
CoRR Vol. abs/2001.04197 2020 Research paper (scientific journal)
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Multi-Modality Behavioral Influence Analysis for Personalized Recommendations in Health Social Media Environment
X. Zhou, W. Liang, I. Kevin, K. Wang, S. Shimizu
IEEE Transactions on Computational Social Systems Vol. 6 No. 5 p. 888-897 2019/10/07 Research paper (scientific journal)
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Analysis of cause-effect inference by comparing regression errors
Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
PeerJ Computer Science Vol. 5 No. 1 p. 900-169 2019/01/21 Research paper (scientific journal)
Publisher: PMLR
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Personalization recommendation algorithm based on trust correlation degree and matrix factorization
Weimin Li, Xiaokang Zhou, Shohei Shimizu, Mingjun Xin, Jiulei Jiang, Honghao Gao, Qun Jin
IEEE Access Vol. 7 p. 45451-45459 2019/01/01 Research paper (scientific journal)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
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A novel personalized recommendation algorithm based on trust relevancy degree
Weimin Li, Heng Zhu, Xiaokang Zhou, Shohei Shimizu, Mingjun Xin, Qun Jin
Proc. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) p. 418-422 2018/08 Research paper (international conference proceedings)
Publisher: IEEE Computer Society
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A novel principle for causal inference in data with small error variance
Patrick Bloebaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schoelkopf
JMLR Workshop and Conference Proceedings, AISTATS2018 (Proc. 21st International Conference on Artificial Intelligence and Statistics) Vol. 84 p. 900-909 2018/04 Research paper (international conference proceedings)
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Cause-Effect Inference by Comparing Regression Errors.
Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
International Conference on Artificial Intelligence and Statistics(AISTATS) Vol. 84 p. 900-909 2018 Research paper (international conference proceedings)
Publisher: PMLR
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Analysis of Cause-Effect Inference via Regression Errors.
Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
CoRR Vol. abs/1802.06698 2018 Research paper (scientific journal)
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Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data.
Chao Li, Shohei Shimizu
CoRR Vol. abs/1802.05889 2018 Research paper (scientific journal)
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Learning instrumental variables with structural and non-Gaussianity assumptions
Ricardo Silva, Shohei Shimizu
Journal of Machine Learning Research Vol. 18 p. 1-49 2017/11/17 Research paper (scientific journal)
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Estimation of interventional effects of features on prediction
Patrick Blobaum, Shohei Shimizu
Proc. 2017 IEEE Machine Learning for Signal Processing Workshop (MLSP2017) Vol. 1 p. 1-6 2017/09 Research paper (international conference proceedings)
Publisher: IEEE
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Error asymmetry in causal and anticausal regression
Patrick Blobaum, Takashi Washio, Shohei Shimizu
Behaviormetrika Vol. abs/1610.03263 No. 2 p. 491-512 2017/04 Research paper (scientific journal)
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A novel principle for causal inference in data with small error variance
Patrick Blobaum, Shohei Shimizu, Takashi Washio
Proc. 25 th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2017) Vol. 1 p. 347-352 2017/04 Research paper (international conference proceedings)
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Special feature: recent developments in causal discovery and inference
Shohei Shimizu
Behaviormetrika Vol. 44 No. 1 p. 135-136 2017/01/01 Research paper (scientific journal)
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Visualizing Shiga Prefecture using RESAS: cloud-based analysis system with government open big data
Jong chan Lee, Tetsuto Himeno, Shohei Shimizu, Takuma Tanaka, Akimichi Takemura
Proc. 2nd International Conference on Big Data, Cloud Computing, and Data Science (BCD2017) 2017 Research paper (international conference proceedings)
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A Non-Gaussian Approach for Causal Discovery in the Presence of Hidden Common Causes.
Shohei Shimizu
Advanced Methodologies for Bayesian Networks - Second International Workshop(AMBN@JSAI-isAI) Vol. 9505 p. 222-233 2015 Research paper (international conference proceedings)
Publisher: Springer
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Discriminative and generative models in causal and anticausal settings
Patrick Blöbaum, Shohei Shimizu, Takashi Washio
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9505 p. 209-221 2015 Research paper (international conference proceedings)
Publisher: Springer Verlag
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Bayesian Estimation of Causal Direction in Acyclic Structural Equation Models with Individual-specific Confounder Variables and Non-Gaussian Distributions
Shohei Shimizu, Kenneth Bollen
JOURNAL OF MACHINE LEARNING RESEARCH Vol. 15 No. 1 p. 2629-2652 2014/08 Research paper (scientific journal)
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A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model.
Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara
CoRR Vol. abs/1408.2038 2014 Research paper (scientific journal)
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Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM.
Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
CoRR Vol. abs/1401.5636 2014 Research paper (scientific journal)
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ESTIMATION OF CAUSAL STRUCTURES IN LONGITUDINAL DATA USING NON-GAUSSIANITY
Kento Kadowaki, Shohei Shimizu, Takashi Washio
2013 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) p. 1-6 2013 Research paper (international conference proceedings)
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Discovery of non-gaussian linear causal models using ICA
Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer
CoRR Vol. abs/1207.1413 2012 Research paper (scientific journal)
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Causal discovery of linear acyclic models with arbitrary distributions
Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
CoRR Vol. abs/1206.3260 2012 Research paper (scientific journal)
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Discovering causal structures in binary exclusive-or skew acyclic models
Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
CoRR Vol. abs/1202.3736 p. 373-382 2012 Research paper (scientific journal)
Publisher: AUAI Press
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Estimation of causal orders in a linear non-Gaussian acyclic model: A method robust against latent confounders
Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 7552 No. 1 p. 491-498 2012 Research paper (international conference proceedings)
Publisher: Springer
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Bootstrap confidence intervals in DirectLiNGAM
Kittitat Thamvitayakul, Shohei Shimizu, Tsuyoshi Ueno, Takashi Washio, Tatsuya Tashiro
12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012) p. 659-668 2012 Research paper (international conference proceedings)
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Analyzing relationships among ARMA processes based on non-Gaussianity of external influences
Yoshinobu Kawahara, Shohei Shimizu, Takashi Washio
NEUROCOMPUTING Vol. 74 No. 12-13 p. 2212-2221 2011/06 Research paper (scientific journal)
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DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvarinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenneth Bollen
JOURNAL OF MACHINE LEARNING RESEARCH Vol. 12 p. 1225-1248 2011/04 Research paper (scientific journal)
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Estimating exogenous variables in data with more variables than observations.
Yasuhiro Sogawa, Shohei Shimizu, Teppei Shimamura, Aapo Hyvärinen, Takashi Washio, Seiya Imoto
Neural Networks Vol. 24 No. 8 p. 875-880 2011 Research paper (scientific journal)
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Discovering causal structures in binary exclusive-or skew acyclic models.
Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
UAI 2011(UAI) p. 373-382 2011 Research paper (international conference proceedings)
Publisher: AUAI Press
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Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
Aapo Hyvarinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer
JOURNAL OF MACHINE LEARNING RESEARCH Vol. 11 p. 1709-1731 2010/05 Research paper (scientific journal)
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Discovery of Exogenous Variables in Data with More Variables Than Observations.
Yasuhiro Sogawa, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio, Teppei Shimamura, Seiya Imoto
Artificial Neural Networks - ICANN 2010 - 20th International Conference Vol. 6352 LNCS No. PART 1 p. 67-76 2010 Research paper (international conference proceedings)
Publisher: Springer
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GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables
Yoshinobu Kawahara, Kenneth Bollen, Shohei Shimizu, Takashi Washio
CoRR Vol. abs/1006.5041 2010 Research paper (scientific journal)
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Assessing Statistical Reliability of LiNGAM via Multiscale Bootstrap
Yusuke Komatsu, Shohei Shimizu, Hidetoshi Shimodaira
ARTIFICIAL NEURAL NETWORKS (ICANN 2010), PT III Vol. 6354 No. PART 3 p. 309-314 2010 Research paper (international conference proceedings)
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An experimental comparison of linear non-Gaussian causal discovery methods and their variants
Yasuhiro Sogawa, Shohei Shimizu, Yoshinobu Kawahara, Takashi Washio
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 p. 1-8 2010 Research paper (international conference proceedings)
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Use of Prior Knowledge in a Non-Gaussian Method for Learning Linear Structural Equation Models
Takanori Inazumi, Shohei Shimizu, Takashi Washio
LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION Vol. 6365 p. 221-228 2010 Research paper (international conference proceedings)
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Estimation of linear non-Gaussian acyclic models for latent factors.
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen
Neurocomputing Vol. 72 No. 7-9 p. 2024-2027 2009 Research paper (scientific journal)
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Estimation of causal effects using linear non-Gaussian causal models with hidden variables
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen, Markus Palviainen
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING Vol. 49 No. 2 p. 362-378 2008/10 Research paper (scientific journal)
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Causal discovery of linear acyclic models with arbitrary distributions.
Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
UAI 2008(UAI) p. 282-289 2008 Research paper (international conference proceedings)
Publisher: AUAI Press
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Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity.
Aapo Hyvärinen, Shohei Shimizu, Patrik O. Hoyer
Machine Learning(ICML) p. 424-431 2008 Research paper (international conference proceedings)
Publisher: ACM
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Discovery of linear non-gaussian acyclic models in the presence of latent classes
Shohei Shimizu, Aapo Hyvärinen
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 4984 No. 1 p. 752-761 2008 Research paper (international conference proceedings)
Publisher: Springer
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A linear non-Gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvarinen, Antti Kerminen
JOURNAL OF MACHINE LEARNING RESEARCH Vol. 7 p. 2003-2030 2006/10 Research paper (scientific journal)
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A linear non-gaussian acyclic model for causal discovery
Shimizu, S., Hoyer, P.O., Hyv{\"a}rinen, A., Kerminen, A.
Journal of Machine Learning Research Vol. 7 2006 Research paper (scientific journal)
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Estimation of linear, non-gaussian causal models in the presence of confounding latent variables
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
CoRR Vol. abs/cs/0603038 2006 Research paper (scientific journal)
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Estimation of linear, non-gaussian causal models in the presence of confounding latent variables.
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
Third European Workshop on Probabilistic Graphical Models p. 155-162 2006 Research paper (international conference proceedings)
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Testing Significance of Mixing and Demixing Coefficients in ICA.
Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer, Antti J. Kerminen
Independent Component Analysis and Blind Signal Separation(ICA) Vol. 3889 LNCS p. 901-908 2006 Research paper (international conference proceedings)
Publisher: Springer
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New Permutation Algorithms for Causal Discovery Using ICA.
Patrik O. Hoyer, Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Antti J. Kerminen
Independent Component Analysis and Blind Signal Separation(ICA) Vol. 3889 LNCS p. 115-122 2006 Research paper (international conference proceedings)
Publisher: Springer
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A quasi-stochastic gradient algorithm for variance-dependent component analysis
Aapo Hyvarinen, Shohei Shimizu
ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 2 Vol. 4132 p. 211-220 2006 Research paper (scientific journal)
