Shohei Shimizu
Orcid: 0000-0002-1931-0733
According to our database1,
Shohei Shimizu
authored at least 72 papers
between 2005 and 2024.
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Bibliography
2024
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating.
CoRR, 2024
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach.
CoRR, 2024
Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data.
CoRR, 2024
2023
Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networks.
IEEE J. Sel. Areas Commun., October, 2023
Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications.
IEEE Trans. Comput. Soc. Syst., August, 2023
IEEE Trans. Neural Networks Learn. Syst., May, 2023
CoRR, 2023
Proceedings of the KDD'23 Workshop on Causal Discovery, 2023
Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling.
Proceedings of the Conference on Causal Learning and Reasoning, 2023
Proceedings of the AAAI Bridge Program on Continual Causality, 2023
2022
Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems.
IEEE Trans. Ind. Informatics, 2022
IEEE Trans. Big Data, 2022
A Survey on Integrity Auditing for Data Storage in the Cloud: From Single Copy to Multiple Replicas.
IEEE Trans. Big Data, 2022
Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System.
IEEE Internet Things J., 2022
Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders.
Int. J. Data Sci. Anal., 2022
Proceedings of the International Joint Conference on Neural Networks, 2022
Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022
Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022
2021
Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems.
IEEE Trans. Ind. Informatics, 2021
Digit. Commun. Networks, 2021
CoRR, 2021
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021
Estimating individual-level optimal causal interventions combining causal models and machine learning models.
Proceedings of the KDD 2021 Workshop on Causal Discovery, 2021
Proceedings of the Causal Analysis Workshop Series, 2021
2020
Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders.
CoRR, 2020
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020
2019
Multi-Modality Behavioral Influence Analysis for Personalized Recommendations in Health Social Media Environment.
IEEE Trans. Comput. Soc. Syst., 2019
PeerJ Comput. Sci., 2019
Personalization Recommendation Algorithm Based on Trust Correlation Degree and Matrix Factorization.
IEEE Access, 2019
2018
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data.
CoRR, 2018
Proceedings of the 2018 IEEE 16th Intl Conf on Dependable, 2018
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018
2017
J. Mach. Learn. Res., 2017
Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, 2017
Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017
2016
2015
A Non-Gaussian Approach for Causal Discovery in the Presence of Hidden Common Causes.
Proceedings of the Advanced Methodologies for Bayesian Networks, 2015
Proceedings of the Advanced Methodologies for Bayesian Networks, 2015
2014
Neural Comput., 2014
Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions.
J. Mach. Learn. Res., 2014
2013
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2013
2012
Proceedings of the 12th IEEE International Conference on Data Mining Workshops, 2012
Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012
2011
Neural Networks, 2011
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model.
J. Mach. Learn. Res., 2011
Analyzing relationships among ARMA processes based on non-Gaussianity of external influences.
Neurocomputing, 2011
Proceedings of the UAI 2011, 2011
2010
J. Mach. Learn. Res., 2010
An experimental comparison of linear non-Gaussian causal discovery methods and their variants.
Proceedings of the International Joint Conference on Neural Networks, 2010
Proceedings of the Artificial Neural Networks - ICANN 2010, 2010
Proceedings of the Artificial Neural Networks - ICANN 2010, 2010
Use of Prior Knowledge in a Non-Gaussian Method for Learning Linear Structural Equation Models.
Proceedings of the Latent Variable Analysis and Signal Separation, 2010
2009
Neurocomputing, 2009
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model.
Proceedings of the UAI 2009, 2009
2008
Estimation of causal effects using linear non-Gaussian causal models with hidden variables.
Int. J. Approx. Reason., 2008
Proceedings of the UAI 2008, 2008
Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity.
Proceedings of the Machine Learning, 2008
2007
Proceedings of the Neural Information Processing, 14th International Conference, 2007
2006
Comput. Stat. Data Anal., 2006
Estimation of linear, non-gaussian causal models in the presence of confounding latent variables.
Proceedings of the Third European Workshop on Probabilistic Graphical Models, 2006
Proceedings of the Artificial Neural Networks, 2006
Proceedings of the Independent Component Analysis and Blind Signal Separation, 2006
Proceedings of the Independent Component Analysis and Blind Signal Separation, 2006
2005
Proceedings of the UAI '05, 2005