Shohei Shimizu

发表

Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes

Aapo Hyvärinen, Shohei Shimizu, Shohei Shimizu, 2007, ICONIP.

Privacy preservation in permissionless blockchain: A survey

Zheng Yan, Xiaokang Zhou, Wei Feng, 2020, Digit. Commun. Networks.

Analysis of Cause-Effect Inference via Regression Errors pdf

Bernhard Schölkopf, Takashi Washio, Dominik Janzing, 2018, ArXiv.

Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions

Ricardo Silva, Shohei Shimizu, Shohei Shimizu, 2017, J. Mach. Learn. Res..

B4SDC: A Blockchain System for Security Data Collection in MANETs

Zheng Yan, Shohei Shimizu, Gao Liu, 2022, IEEE Transactions on Big Data.

Analysis of cause-effect inference by comparing regression errors

Bernhard Schölkopf, Takashi Washio, Dominik Janzing, 2018, PeerJ Comput. Sci..

Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders

Aapo Hyvärinen, Takashi Washio, Shohei Shimizu, 2012, ICANN.

DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model

Aapo Hyvärinen, Yoshinobu Kawahara, Patrik O. Hoyer, 2011, J. Mach. Learn. Res..

A Non-Gaussian Approach for Causal Discovery in the Presence of Hidden Common Causes

Shohei Shimizu, Shohei Shimizu, 2015, AMBN@JSAI-isAI.

Cause-Effect Inference by Comparing Regression Errors

Bernhard Schölkopf, Takashi Washio, Dominik Janzing, 2018, AISTATS.

Lingam: Non-Gaussian Methods for Estimating Causal Structures

Shohei Shimizu, Shohei Shimizu, 2014, Behaviormetrika.

A Linear Non-Gaussian Acyclic Model for Causal Discovery

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2006, J. Mach. Learn. Res..

Discriminative and Generative Models in Causal and Anticausal Settings

Takashi Washio, Shohei Shimizu, Patrick Blöbaum, 2015, AMBN@JSAI-isAI.

Learning LiNGAM based on data with more variables than observations

Shohei Shimizu, Shohei Shimizu, 2012, 1208.4183.

Analyzing relationships among ARMA processes based on non-Gaussianity of external influences

Yoshinobu Kawahara, Takashi Washio, Shohei Shimizu, 2011, Neurocomputing.

Causal discovery of linear acyclic models with arbitrary distributions

Aapo Hyvärinen, Patrik O. Hoyer, Richard Scheines, 2008, UAI.

Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data pdf

Chao Li, Shohei Shimizu, Shohei Shimizu, 2018, ArXiv.

A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model pdf

Aapo Hyvärinen, Yoshinobu Kawahara, Shohei Shimizu, 2009, UAI.

Estimation of Post-Nonlinear Causal Models Using Autoencoding Structure

Shohei Shimizu, Kento Uemura, 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Estimation of causal effects using linear non-Gaussian causal models with hidden variables

Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen, 2008, Int. J. Approx. Reason..

Estimation of linear, non-gaussian causal models in the presence of confounding latent variables

Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen, 2006, Probabilistic Graphical Models.

Estimation of interventional effects of features on prediction

Shohei Shimizu, Patrick Blöbaum, Shohei Shimizu, 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

Causal Discovery with Multi-Domain LiNGAM for Latent Factors pdf

Ruichu Cai, Shohei Shimizu, Zhifeng Hao, 2020, ArXiv.

Discovery of Non-gaussian Linear Causal Models using ICA

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2005, UAI.

Bootstrap Confidence Intervals in DirectLiNGAM

Takashi Washio, Shohei Shimizu, Tsuyoshi Ueno, 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

ParceLiNGAM: A Causal Ordering Method Robust Against Latent Confounders

Aapo Hyvärinen, Takashi Washio, Shohei Shimizu, 2013, Neural Computation.

RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders

Shohei Shimizu, Takashi Nicholas Maeda, Shohei Shimizu, 2020, AISTATS.

Discovering causal structures in binary exclusive-or skew acyclic models

Akihiro Yamamoto, Joe Suzuki, Yoshinobu Kawahara, 2011, UAI.

Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders pdf

Shohei Shimizu, Takashi Nicholas Maeda, Shohei Shimizu, 2020, ArXiv.

New Permutation Algorithms for Causal Discovery Using ICA

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2006, ICA.

Estimation of causal structures in longitudinal data using non-Gaussianity

Takashi Washio, Shohei Shimizu, Kento Kadowaki, 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Discovery of Exogenous Variables in Data with More Variables Than Observations

Aapo Hyvärinen, Teppei Shimamura, Takashi Washio, 2009, ICANN.

Non‐Gaussian Structural Equation Models for Causal Discovery

Shohei Shimizu, Shohei Shimizu, 2016 .

Testing Significance of Mixing and Demixing Coefficients in ICA

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2006, ICA.

Use of Prior Knowledge in a Non-Gaussian Method for Learning Linear Structural Equation Models

Takashi Washio, Shohei Shimizu, Takanori Inazumi, 2010, LVA/ICA.

A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree

Weimin Li, Qun Jin, Xiaokang Zhou, 2018, 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).

Assessing Statistical Reliability of LiNGAM via Multiscale Bootstrap

Yusuke Komatsu, Shohei Shimizu, Hidetoshi Shimodaira, 2010, ICANN.

Multi-Modality Behavioral Influence Analysis for Personalized Recommendations in Health Social Media Environment

Kevin I-Kai Wang, Xiaokang Zhou, Shohei Shimizu, 2019, IEEE Transactions on Computational Social Systems.

A novel principle for causal inference in data with small error variance

Takashi Washio, Shohei Shimizu, Patrick Blöbaum, 2017, ESANN.

An experimental comparison of linear non-Gaussian causal discovery methods and their variants

Yoshinobu Kawahara, Takashi Washio, Shohei Shimizu, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Special feature: recent developments in causal discovery and inference

Shohei Shimizu, 2017 .

Estimation of linear non-Gaussian acyclic models for latent factors

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2009, Neurocomputing.

Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2008, ICML '08.

Non-Gaussian Methods for Causal Structure Learning

Shohei Shimizu, Shohei Shimizu, 2018, Prevention Science.

Finding a causal ordering via independent component analysis

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2006, Comput. Stat. Data Anal..

Estimating exogenous variables in data with more variables than observations

Aapo Hyvärinen, Teppei Shimamura, Takashi Washio, 2011, Neural Networks.

Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity

Aapo Hyvärinen, Kun Zhang, Patrik O. Hoyer, 2010, J. Mach. Learn. Res..

Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM pdf

Akihiro Yamamoto, Joe Suzuki, Yoshinobu Kawahara, 2014, ArXiv.

Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions

Shohei Shimizu, Kenneth Bollen, K. Bollen, 2014, J. Mach. Learn. Res..

GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables pdf

Yoshinobu Kawahara, Takashi Washio, Shohei Shimizu, 2010, ArXiv.

Personalization Recommendation Algorithm Based on Trust Correlation Degree and Matrix Factorization

Honghao Gao, Qun Jin, Xiaokang Zhou, 2019, IEEE Access.

Joint estimation of linear non-Gaussian acyclic models

Shohei Shimizu, Shohei Shimizu, 2011, Neurocomputing.

A Quasi-stochastic Gradient Algorithm for Variance-Dependent Component Analysis

Aapo Hyvärinen, Shohei Shimizu, Shohei Shimizu, 2006, ICANN.

Error asymmetry in causal and anticausal regression

Takashi Washio, Shohei Shimizu, Patrick Blöbaum, 2016, Behaviormetrika.

Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems

Laurence T. Yang, Qun Jin, Xiaokang Zhou, 2022, IEEE Transactions on Industrial Informatics.

Causal additive models with unobserved variables

Shohei Shimizu, Takashi Nicholas Maeda, Shohei Shimizu, 2021, UAI.

Causal Discovery with Multi-Domain LiNGAM for Latent Factors

Ruichu Cai, Zhifeng Hao, Shohei Shimizu, 2021, IJCAI.

Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders pdf

Shohei Shimizu, Takashi Nicholas Maeda, 2021, International Journal of Data Science and Analytics.

Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems

Laurence T. Yang, Qun Jin, Xiaokang Zhou, 2021, IEEE Transactions on Industrial Informatics.

Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

Jianhua Ma, Qun Jin, Xiaokang Zhou, 2021, IEEE Transactions on Industrial Informatics.

Discovery of Causal Additive Models in the Presence of Unobserved Variables pdf

Shohei Shimizu, Takashi Nicholas Maeda, Shohei Shimizu, 2021, ArXiv.

Examination of Independence in Independent Component Analysis

Shohei Shimizu, Yutaka Kano, Y. Kano, 2003 .

Use of non-normality in structural equation modeling: Application to direction of causation

Shohei Shimizu, Yutaka Kano, Y. Kano, 2008 .

FACTOR ROTATION AND ICA

Yusuke Miyamoto, Shohei Shimizu, Yutaka Kano, 2003 .