Other names清水 昌平
Shiga University & RIKEN
Verified email at - Homepage
Cited by
Cited by
A linear non-Gaussian acyclic model for causal discovery
S Shimizu, PO Hoyer, A Hyvärinen, A Kerminen
Journal of Machine Learning Research 7, 2003-2030, 2006
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
S Shimizu, T Inazumi, Y Sogawa, A Hyvarinen, Y Kawahara, T Washio, ...
Journal of Machine Learning Research 12, 1225-1248, 2011
Estimation of a structural vector autoregression model using non-Gaussianity
A Hyvärinen, K Zhang, S Shimizu, PO Hoyer
Journal of Machine Learning Research 11, 1709-1731, 2010
Siamese neural network based few-shot learning for anomaly detection in industrial cyber-physical systems
X Zhou, W Liang, S Shimizu, J Ma, Q Jin
IEEE Transactions on Industrial Informatics 17 (8), 5790-5798, 2020
Estimation of causal effects using linear non-Gaussian causal models with hidden variables
PO Hoyer, S Shimizu, AJ Kerminen, M Palviainen
International Journal of Approximate Reasoning 49 (2), 362-378, 2008
Intelligent small object detection for digital twin in smart manufacturing with industrial cyber-physical systems
X Zhou, X Xu, W Liang, Z Zeng, S Shimizu, LT Yang, Q Jin
IEEE Transactions on Industrial Informatics 18 (2), 1377-1386, 2021
Hierarchical adversarial attacks against graph-neural-network-based IoT network intrusion detection system
X Zhou, W Liang, W Li, K Yan, S Shimizu, I Kevin, K Wang
IEEE Internet of Things Journal 9 (12), 9310-9319, 2021
Privacy preservation in permissionless blockchain: A survey
L Peng, W Feng, Z Yan, Y Li, X Zhou, S Shimizu
Digital Communications and Networks 7 (3), 295-307, 2021
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 6 (5), 888-897, 2019
LiNGAM: Non-Gaussian methods for estimating causal structures
S Shimizu
Behaviormetrika 41 (1), 65-98, 2014
Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity
A Hyvärinen, S Shimizu, PO Hoyer
The 25th International Conference on Machine learning (ICML2008), 424-431, 2008
Causal discovery of linear acyclic models with arbitrary distributions
PO Hoyer, A Hyvärinen, R Scheines, P Spirtes, J Ramsey, G Lacerda, ...
The 24th Conference on Uncertainty in Artificial Intelligence (UAI2008), 2008
Hierarchical federated learning with social context clustering-based participant selection for internet of medical things applications
X Zhou, X Ye, I Kevin, K Wang, W Liang, NKC Nair, S Shimizu, Z Yan, ...
IEEE Transactions on Computational Social Systems 10 (4), 1742-1751, 2023
Causal inference using nonnormality
Y Kano, S Shimizu
International Symposium on Science of Modeling, the 30th Anniversary of the …, 2003
Cause-effect inference by comparing regression errors
P Blöbaum, D Janzing, T Washio, S Shimizu, B Schölkopf
International Conference on Artificial Intelligence and Statistics …, 2018
Use of non-normality in structural equation modeling: Application to direction of causation
S Shimizu, Y Kano
Journal of Statistical Planning and Inference 138 (11), 3483-3491, 2008
ParceLiNGAM: A causal ordering method robust against latent confounders
T Tashiro, S Shimizu, A Hyvarinen, T Washio
Neural Computation 26 (1), 57-83, 2014
B4SDC: A blockchain system for security data collection in MANETs
G Liu, H Dong, Z Yan, X Zhou, S Shimizu
IEEE transactions on big data 8 (3), 739-752, 2020
Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions
S Shimizu, K Bollen
Journal of Machine Learning Research 15, 2629-2652, 2014
Discovery of non-gaussian linear causal models using ICA
S Shimizu, A Hyvarinen, Y Kano, PO Hoyer
The 21st Conference on Uncertainty in Artificial Intelligence (UAI2005), 2005
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