Shohei Shimizu

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 46

Top Topics

Learning Models

Personalized Recommendation

Observed Variables

Top Venues

J. Mach. Learn. Res.

Publications

Takashi Nicholas Maeda, Shohei Shimizu
Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data. CoRR (2024)
Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models. CLeaR (2024)
Masayuki Takayama, Tadahisa Okuda, Thong Pham, Tatsuyoshi Ikenoue, Shingo Fukuma, Shohei Shimizu, Akiyoshi Sannai
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach. CoRR (2024)
Daisuke Takahashi, Shohei Shimizu, Takuma Tanaka
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating. CoRR (2024)
Xiaokang Zhou, Xuzhe Zheng, Xuesong Cui, Jiashuai Shi, Wei Liang, Zheng Yan, Laurence T. Yang, Shohei Shimizu, Kevin I-Kai Wang
Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networks. IEEE J. Sel. Areas Commun. 41 (10) (2023)
Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu
Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling. CLeaR (2023)
Yi Jiang, Shohei Shimizu
Linkages among the Foreign Exchange, Stock, and Bond Markets in Japan and the United States. CAWS (2023)
Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models. CoRR (2023)
Shusuke Wani, Xiaokang Zhou, Shohei Shimizu
BiLSTM and VAE Enhanced Multi-Task Neural Network for Trust-Aware E-Commerce Product Analysis. TrustCom (2023)
Xiaokang Zhou, Xiaozhou Ye, Kevin I-Kai Wang, Wei Liang, Nirmal-Kumar C. Nair, Shohei Shimizu, Zheng Yan, Qun Jin
Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications. IEEE Trans. Comput. Soc. Syst. 10 (4) (2023)
Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu
Python package for causal discovery based on LiNGAM. J. Mach. Learn. Res. 24 (2023)
Thuc Duy Le, Jiuyong Li, Robert Ness, Sofia Triantafillou, Shohei Shimizu, Peng Cui, Kun Kuang, Jian Pei, Fei Wang, Mattia Prosperi
Preface: The 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision. CDPD (2023)
Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu
Prospects of Continual Causality for Industrial Applications. AAAI Bridge Program (2023)
Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang
Causal-learn: Causal Discovery in Python. CoRR (2023)
Genta Kikuchi, Shohei Shimizu
Structure Learning for Groups of Variables in Nonlinear Time-Series Data with Location-Scale Noise. CAWS (2023)
Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Libo Huang, Shohei Shimizu
Nonlinear Causal Discovery for High-Dimensional Deterministic Data. IEEE Trans. Neural Networks Learn. Syst. 34 (5) (2023)
Yan Zeng, Shohei Shimizu, Hidetoshi Matsui, Fuchun Sun
Causal Discovery for Linear Mixed Data. CLeaR (2022)
Angtai Li, Yu Chen, Zheng Yan, Xiaokang Zhou, Shohei Shimizu
A Survey on Integrity Auditing for Data Storage in the Cloud: From Single Copy to Multiple Replicas. IEEE Trans. Big Data 8 (5) (2022)
Gao Liu, Huidong Dong, Zheng Yan, Xiaokang Zhou, Shohei Shimizu
B4SDC: A Blockchain System for Security Data Collection in MANETs. IEEE Trans. Big Data 8 (3) (2022)
Kento Uemura, Takuya Takagi, Kambayashi Takayuki, Hiroyuki Yoshida, Shohei Shimizu
A Multivariate Causal Discovery based on Post-Nonlinear Model. CLeaR (2022)
Takashi Nicholas Maeda, Shohei Shimizu
Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. Int. J. Data Sci. Anal. 13 (2) (2022)
Xiaokang Zhou, Xuesong Xu, Wei Liang, Zhi Zeng, Shohei Shimizu, Laurence T. Yang, Qun Jin
Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems. IEEE Trans. Ind. Informatics 18 (2) (2022)
Xiaokang Zhou, Wei Liang, Weimin Li, Ke Yan, Shohei Shimizu, Kevin I-Kai Wang
Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System. IEEE Internet Things J. 9 (12) (2022)
Kazuhi Honjo, Xiaokang Zhou, Shohei Shimizu
CNN-GRU Based Deep Learning Model for Demand Forecast in Retail Industry. IJCNN (2022)
Keisuke Kiritoshi, Tomonori Izumitani, Kazuki Koyama, Tomomi Okawachi, Keisuke Asahara, Shohei Shimizu
Estimating individual-level optimal causal interventions combining causal models and machine learning models. CD@KDD (2021)
Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao
Causal Discovery with Multi-Domain LiNGAM for Latent Factors. CAWS (2021)
Li Peng, Wei Feng, Zheng Yan, Yafeng Li, Xiaokang Zhou, Shohei Shimizu
Privacy preservation in permissionless blockchain: A survey. Digit. Commun. Networks 7 (3) (2021)
Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao
Causal Discovery with Multi-Domain LiNGAM for Latent Factors. IJCAI (2021)
Takashi Nicholas Maeda, Shohei Shimizu
Causal additive models with unobserved variables. UAI (2021)
Takashi Nicholas Maeda, Shohei Shimizu
Discovery of Causal Additive Models in the Presence of Unobserved Variables. CoRR (2021)
Xiaokang Zhou, Wei Liang, Shohei Shimizu, Jianhua Ma, Qun Jin
Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems. IEEE Trans. Ind. Informatics 17 (8) (2021)
Takashi Nicholas Maeda, Shohei Shimizu
Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. CoRR (2020)
Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao
Causal Discovery with Multi-Domain LiNGAM for Latent Factors. CoRR (2020)
Takashi Nicholas Maeda, Shohei Shimizu
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. AISTATS (2020)
Kento Uemura, Shohei Shimizu
Estimation of Post-Nonlinear Causal Models Using Autoencoding Structure. ICASSP (2020)
Weimin Li, Xiaokang Zhou, Shohei Shimizu, Mingjun Xin, Jiulei Jiang, Honghao Gao, Qun Jin
Personalization Recommendation Algorithm Based on Trust Correlation Degree and Matrix Factorization. IEEE Access 7 (2019)
Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
Analysis of cause-effect inference by comparing regression errors. PeerJ Comput. Sci. 5 (2019)
Xiaokang Zhou, Wei Liang, Kevin I-Kai Wang, Shohei Shimizu
Multi-Modality Behavioral Influence Analysis for Personalized Recommendations in Health Social Media Environment. IEEE Trans. Comput. Soc. Syst. 6 (5) (2019)
Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
Analysis of Cause-Effect Inference via Regression Errors. CoRR (2018)
Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
Cause-Effect Inference by Comparing Regression Errors. AISTATS (2018)
Chao Li, Shohei Shimizu
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data. CoRR (2018)
Weimin Li, Heng Zhu, Xiaokang Zhou, Shohei Shimizu, Mingjun Xin, Qun Jin
A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree. DASC/PiCom/DataCom/CyberSciTech (2018)
Patrick Blöbaum, Shohei Shimizu, Takashi Washio
A novel principle for causal inference in data with small error variance. ESANN (2017)
Ricardo Silva, Shohei Shimizu
Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions. J. Mach. Learn. Res. 18 (2017)
Patrick Blöbaum, Shohei Shimizu
Estimation of interventional effects of features on prediction. MLSP (2017)
Patrick Blöbaum, Takashi Washio, Shohei Shimizu
Error Asymmetry in Causal and Anticausal Regression. CoRR (2016)
Shohei Shimizu
A Non-Gaussian Approach for Causal Discovery in the Presence of Hidden Common Causes. AMBN@JSAI-isAI (2015)
Patrick Blöbaum, Shohei Shimizu, Takashi Washio
Discriminative and Generative Models in Causal and Anticausal Settings. AMBN@JSAI-isAI (2015)
Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio
ParceLiNGAM: A Causal Ordering Method Robust Against Latent Confounders. Neural Comput. 26 (1) (2014)
Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM. CoRR (2014)
Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. CoRR (2014)
Shohei Shimizu, Kenneth Bollen
Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. J. Mach. Learn. Res. 15 (1) (2014)
Kento Kadowaki, Shohei Shimizu, Takashi Washio
Estimation of causal structures in longitudinal data using non-Gaussianity. MLSP (2013)
Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio
Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders. ICANN (1) (2012)
Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer
Discovery of non-gaussian linear causal models using ICA CoRR (2012)
Shohei Shimizu
Joint estimation of linear non-Gaussian acyclic models. Neurocomputing 81 (2012)
Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
Causal discovery of linear acyclic models with arbitrary distributions CoRR (2012)
Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
Discovering causal structures in binary exclusive-or skew acyclic models CoRR (2012)
Kittitat Thamvitayakul, Shohei Shimizu, Tsuyoshi Ueno, Takashi Washio, Tatsuya Tashiro
Bootstrap Confidence Intervals in DirectLiNGAM. ICDM Workshops (2012)
Yoshinobu Kawahara, Shohei Shimizu, Takashi Washio
Analyzing relationships among ARMA processes based on non-Gaussianity of external influences. Neurocomputing 74 (12-13) (2011)
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenneth Bollen
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model. J. Mach. Learn. Res. 12 (2011)
Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
Discovering causal structures in binary exclusive-or skew acyclic models. UAI (2011)
Yasuhiro Sogawa, Shohei Shimizu, Teppei Shimamura, Aapo Hyvärinen, Takashi Washio, Seiya Imoto
Estimating exogenous variables in data with more variables than observations. Neural Networks 24 (8) (2011)
Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. J. Mach. Learn. Res. 11 (2010)
Yasuhiro Sogawa, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio, Teppei Shimamura, Seiya Imoto
Discovery of Exogenous Variables in Data with More Variables Than Observations. ICANN (1) (2010)
Yoshinobu Kawahara, Kenneth Bollen, Shohei Shimizu, Takashi Washio
GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables CoRR (2010)
Takanori Inazumi, Shohei Shimizu, Takashi Washio
Use of Prior Knowledge in a Non-Gaussian Method for Learning Linear Structural Equation Models. LVA/ICA (2010)
Yusuke Komatsu, Shohei Shimizu, Hidetoshi Shimodaira
Assessing Statistical Reliability of LiNGAM via Multiscale Bootstrap. ICANN (3) (2010)
Yasuhiro Sogawa, Shohei Shimizu, Yoshinobu Kawahara, Takashi Washio
An experimental comparison of linear non-Gaussian causal discovery methods and their variants. IJCNN (2010)
Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. UAI (2009)
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen
Estimation of linear non-Gaussian acyclic models for latent factors. Neurocomputing 72 (7-9) (2009)
Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
Causal discovery of linear acyclic models with arbitrary distributions. UAI (2008)
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen, Markus Palviainen
Estimation of causal effects using linear non-Gaussian causal models with hidden variables. Int. J. Approx. Reason. 49 (2) (2008)
Aapo Hyvärinen, Shohei Shimizu, Patrik O. Hoyer
Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity. ICML (2008)
Shohei Shimizu, Aapo Hyvärinen
Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes. ICONIP (1) (2007)
Patrik O. Hoyer, Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Antti J. Kerminen
New Permutation Algorithms for Causal Discovery Using ICA. ICA (2006)
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
Estimation of linear, non-gaussian causal models in the presence of confounding latent variables CoRR (2006)
Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer, Antti J. Kerminen
Testing Significance of Mixing and Demixing Coefficients in ICA. ICA (2006)
Shohei Shimizu, Aapo Hyvärinen, Patrik O. Hoyer, Yutaka Kano
Finding a causal ordering via independent component analysis. Comput. Stat. Data Anal. 50 (11) (2006)
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti J. Kerminen
A Linear Non-Gaussian Acyclic Model for Causal Discovery. J. Mach. Learn. Res. 7 (2006)
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
Estimation of linear, non-gaussian causal models in the presence of confounding latent variables. Probabilistic Graphical Models (2006)
Aapo Hyvärinen, Shohei Shimizu
A Quasi-stochastic Gradient Algorithm for Variance-Dependent Component Analysis. ICANN (2) (2006)
Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer
Discovery of Non-gaussian Linear Causal Models using ICA. UAI (2005)