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 | 1835 | 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 | 589 | 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 | 416 | 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 | 259 | 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 | 242 | 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 | 217 | 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 | 210 | 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 | 182 | 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 | 130 | 2019 |
LiNGAM: Non-Gaussian methods for estimating causal structures S Shimizu Behaviormetrika 41 (1), 65-98, 2014 | 120 | 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 | 100 | 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 | 93 | 2023 |
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 | 91* | 2008 |
Causal inference using nonnormality Y Kano, S Shimizu International Symposium on Science of Modeling, the 30th Anniversary of the …, 2003 | 91 | 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 | 87 | 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 | 87 | 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 | 80 | 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 | 73 | 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 | 66 | 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 | 63* | 2005 |