Shohei Shimizu

100 papers A* 4A 7B 6Misc 1Journal 55Unranked 26

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Discovering Linear Non-Gaussian Models for All Categories of Missing Data (Student Abstract). AAAI Matteo Ceriscioli, Shohei Shimizu, Karthika Mohan
2026	A*	`conf`	I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables. AAAI Hirofumi Suzuki, Kentaro Kanamori, Takuya Takagi, Thong Pham, Takashi Nicholas Maeda, Shohei Shimizu
2026	J	`jnl`	I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables. CoRR Hirofumi Suzuki, Kentaro Kanamori, Takuya Takagi, Thong Pham, Takashi Nicholas Maeda, Shohei Shimizu
2026	J	`jnl`	Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints. CoRR Tadahisa Okuda, Shohei Shimizu, Thong Pham, Tatsuyoshi Ikenoue, Shingo Fukuma
2025	J	`jnl`	Causal Additive Models with Unobserved Causal Paths and Backdoor Paths. CoRR Thong Pham, Takashi Nicholas Maeda, Shohei Shimizu
2025	J	`jnl`	Causal models and prediction in cell line perturbation experiments. BMC Bioinform. James P. Long, Yumeng Yang, Shohei Shimizu, Thong Pham, Kim-Anh Do
2025	B	`conf`	Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis. IJCNN Hiroshi Yokoyama, Ryusei Shingaki, Kaneharu Nishino, Shohei Shimizu, Thong Pham
2025	J	`jnl`	Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Data. CoRR Takashi Nicholas Maeda, Shohei Shimizu, Hidetoshi Matsui
2025	J	`jnl`	Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding. Trans. Mach. Learn. Res. Yoshimitsu Morinishi, Shohei Shimizu
2025	J	`jnl`	Information Theoretic Learning-Enhanced Dual-Generative Adversarial Networks With Causal Representation for Robust OOD Generalization. IEEE Trans. Neural Networks Learn. Syst. Xiaokang Zhou, Xuzhe Zheng, Tian Shu, Wei Liang, Kevin I-Kai Wang, Lianyong Qi, Shohei Shimizu, Qun Jin
2025	J	`jnl`	Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach. Trans. Mach. Learn. Res. Masayuki Takayama, Tadahisa Okuda, Thong Pham, Tatsuyoshi Ikenoue, Shingo Fukuma, Shohei Shimizu, Akiyoshi Sannai
2025	J	`jnl`	Novel MITM attack scheme based on built-in negotiation for blockchain-based digital twins. Digit. Commun. Networks Xin Liu, Rui Zhou, Shohei Shimizu, Rui Chong, Qingguo Zhou, Xiaokang Zhou
2024	J	`jnl`	Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis. CoRR Hiroshi Yokoyama, Ryusei Shingaki, Kaneharu Nishino, Shohei Shimizu, Thong Pham
2024	J	`jnl`	Causal-learn: Causal Discovery in Python. J. Mach. Learn. Res. Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang
2024	B	`conf`	Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating. IJCNN Daisuke Takahashi, Shohei Shimizu, Takuma Tanaka
2024	J	`jnl`	Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating. CoRR Daisuke Takahashi, Shohei Shimizu, Takuma Tanaka
2024	J	`jnl`	Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach. CoRR Masayuki Takayama, Tadahisa Okuda, Thong Pham, Tatsuyoshi Ikenoue, Shingo Fukuma, Shohei Shimizu, Akiyoshi Sannai
2024	—	`conf`	Multi-Domain and Multi-View Oriented Deep Neural Network for Sentiment Analysis in Large Language Models. iThings/GreenCom/CPSCom/SmartData/Cybermatics Keito Inoshita, Xiaokang Zhou, Shohei Shimizu
2024	—	`conf`	Scalable Counterfactual Distribution Estimation in Multivariate Causal Models. CLeaR Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le
2024	J	`jnl`	Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data. CoRR Takashi Nicholas Maeda, Shohei Shimizu
2023	B	`conf`	BiLSTM and VAE Enhanced Multi-Task Neural Network for Trust-Aware E-Commerce Product Analysis. TrustCom Shusuke Wani, Xiaokang Zhou, Shohei Shimizu
2023	—	`conf`	Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling. CLeaR Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu
2023	J	`jnl`	Causal-learn: Causal Discovery in Python. CoRR Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang
2023	J	`jnl`	Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networks. IEEE J. Sel. Areas Commun. Xiaokang Zhou, Xuzhe Zheng, Xuesong Cui, Jiashuai Shi, Wei Liang, Zheng Yan, Laurence T. Yang, Shohei Shimizu, Kevin I-Kai Wang
2023	J	`jnl`	Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications. IEEE Trans. Comput. Soc. Syst. Xiaokang Zhou, Xiaozhou Ye, Kevin I-Kai Wang, Wei Liang, Nirmal-Kumar C. Nair, Shohei Shimizu, Zheng Yan, Qun Jin
2023	—	`conf`	Linkages among the Foreign Exchange, Stock, and Bond Markets in Japan and the United States. CAWS Yi Jiang, Shohei Shimizu
2023	J	`jnl`	Nonlinear Causal Discovery for High-Dimensional Deterministic Data. IEEE Trans. Neural Networks Learn. Syst. Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Libo Huang, Shohei Shimizu
2023	—	`conf`	Preface: The 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision. CDPD Thuc Duy Le, Jiuyong Li, Robert Osazuwa Ness, Sofia Triantafillou, Shohei Shimizu, Peng Cui, Kun Kuang, Jian Pei, Fei Wang, Mattia Prosperi
2023	—	`conf`	Prospects of Continual Causality for Industrial Applications. AAAI Bridge Program Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu
2023	J	`jnl`	Python package for causal discovery based on LiNGAM. J. Mach. Learn. Res. Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu
2023	J	`jnl`	Scalable Counterfactual Distribution Estimation in Multivariate Causal Models. CoRR Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le
2023	—	`conf`	Structure Learning for Groups of Variables in Nonlinear Time-Series Data with Location-Scale Noise. CAWS Genta Kikuchi, Shohei Shimizu
2023	—	`ed.`	The KDD'23 Workshop on Causal Discovery, Prediction and Decision, 07 August 2023, Long Beach, CA, USA CDPD Thuc Duy Le, Jiuyong Li, Robert Osazuwa Ness, Sofia Triantafillou, Shohei Shimizu, Peng Cui, Kun Kuang, Jian Pei, Fei Wang, Mattia Prosperi
2022	—	`conf`	A Multivariate Causal Discovery based on Post-Nonlinear Model. CLeaR Kento Uemura, Takuya Takagi, Kambayashi Takayuki, Hiroyuki Yoshida, Shohei Shimizu
2022	J	`jnl`	A Survey on Integrity Auditing for Data Storage in the Cloud: From Single Copy to Multiple Replicas. IEEE Trans. Big Data Angtai Li, Yu Chen, Zheng Yan, Xiaokang Zhou, Shohei Shimizu
2022	J	`jnl`	B4SDC: A Blockchain System for Security Data Collection in MANETs. IEEE Trans. Big Data Gao Liu, Huidong Dong, Zheng Yan, Xiaokang Zhou, Shohei Shimizu
2022	B	`conf`	CNN-GRU Based Deep Learning Model for Demand Forecast in Retail Industry. IJCNN Kazuhi Honjo, Xiaokang Zhou, Shohei Shimizu
2022	—	`conf`	Causal Discovery for Linear Mixed Data. CLeaR Yan Zeng, Shohei Shimizu, Hidetoshi Matsui, Fuchun Sun
2022	J	`jnl`	Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System. IEEE Internet Things J. Xiaokang Zhou, Wei Liang, Weimin Li, Ke Yan, Shohei Shimizu, Kevin I-Kai Wang
2022	J	`jnl`	Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems. IEEE Trans. Ind. Informatics Xiaokang Zhou, Xuesong Xu, Wei Liang, Zhi Zeng, Shohei Shimizu, Laurence T. Yang, Qun Jin
2022	J	`jnl`	Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. Int. J. Data Sci. Anal. Takashi Nicholas Maeda, Shohei Shimizu
2021	—	`conf`	Causal Discovery with Multi-Domain LiNGAM for Latent Factors. CAWS Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao
2021	A*	`conf`	Causal Discovery with Multi-Domain LiNGAM for Latent Factors. IJCAI Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao
2021	A	`conf`	Causal additive models with unobserved variables. UAI Takashi Nicholas Maeda, Shohei Shimizu
2021	J	`jnl`	Discovery of Causal Additive Models in the Presence of Unobserved Variables. CoRR Takashi Nicholas Maeda, Shohei Shimizu
2021	—	`conf`	Estimating individual-level optimal causal interventions combining causal models and machine learning models. CD@KDD Keisuke Kiritoshi, Tomonori Izumitani, Kazuki Koyama, Tomomi Okawachi, Keisuke Asahara, Shohei Shimizu
2021	J	`jnl`	Privacy preservation in permissionless blockchain: A survey. Digit. Commun. Networks Li Peng, Wei Feng, Zheng Yan, Yafeng Li, Xiaokang Zhou, Shohei Shimizu
2021	J	`jnl`	Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems. IEEE Trans. Ind. Informatics Xiaokang Zhou, Wei Liang, Shohei Shimizu, Jianhua Ma, Qun Jin
2020	J	`jnl`	Causal Discovery with Multi-Domain LiNGAM for Latent Factors. CoRR Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao
2020	J	`jnl`	Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. CoRR Takashi Nicholas Maeda, Shohei Shimizu
2020	Misc	`conf`	Estimation of Post-Nonlinear Causal Models Using Autoencoding Structure. ICASSP Kento Uemura, Shohei Shimizu
2020	A	`conf`	RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. AISTATS Takashi Nicholas Maeda, Shohei Shimizu
2019	J	`jnl`	Analysis of cause-effect inference by comparing regression errors. PeerJ Comput. Sci. Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
2019	J	`jnl`	Multi-Modality Behavioral Influence Analysis for Personalized Recommendations in Health Social Media Environment. IEEE Trans. Comput. Soc. Syst. Xiaokang Zhou, Wei Liang, Kevin I-Kai Wang, Shohei Shimizu
2019	J	`jnl`	Personalization Recommendation Algorithm Based on Trust Correlation Degree and Matrix Factorization. IEEE Access Weimin Li, Xiaokang Zhou, Shohei Shimizu, Mingjun Xin, Jiulei Jiang, Honghao Gao, Qun Jin
2018	—	`conf`	A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree. DASC/PiCom/DataCom/CyberSciTech Weimin Li, Heng Zhu, Xiaokang Zhou, Shohei Shimizu, Mingjun Xin, Qun Jin
2018	J	`jnl`	Analysis of Cause-Effect Inference via Regression Errors. CoRR Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
2018	A	`conf`	Cause-Effect Inference by Comparing Regression Errors. AISTATS Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
2018	J	`jnl`	Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data. CoRR Chao Li, Shohei Shimizu
2017	B	`conf`	A novel principle for causal inference in data with small error variance. ESANN Patrick Blöbaum, Shohei Shimizu, Takashi Washio
2017	—	`conf`	Estimation of interventional effects of features on prediction. MLSP Patrick Blöbaum, Shohei Shimizu
2017	J	`jnl`	Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions. J. Mach. Learn. Res. Ricardo Silva, Shohei Shimizu
2016	J	`jnl`	Error Asymmetry in Causal and Anticausal Regression. CoRR Patrick Blöbaum, Takashi Washio, Shohei Shimizu
2015	—	`conf`	A Non-Gaussian Approach for Causal Discovery in the Presence of Hidden Common Causes. AMBN@JSAI-isAI Shohei Shimizu
2015	—	`conf`	Discriminative and Generative Models in Causal and Anticausal Settings. AMBN@JSAI-isAI Patrick Blöbaum, Shohei Shimizu, Takashi Washio
2014	J	`jnl`	A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. CoRR Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara
2014	J	`jnl`	Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. J. Mach. Learn. Res. Shohei Shimizu, Kenneth Bollen
2014	J	`jnl`	Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM. CoRR Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
2014	J	`jnl`	ParceLiNGAM: A Causal Ordering Method Robust Against Latent Confounders. Neural Comput. Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio
2013	—	`conf`	Estimation of causal structures in longitudinal data using non-Gaussianity. MLSP Kento Kadowaki, Shohei Shimizu, Takashi Washio
2012	—	`conf`	Bootstrap Confidence Intervals in DirectLiNGAM. ICDM Workshops Kittitat Thamvitayakul, Shohei Shimizu, Tsuyoshi Ueno, Takashi Washio, Tatsuya Tashiro
2012	J	`jnl`	Causal discovery of linear acyclic models with arbitrary distributions CoRR Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
2012	J	`jnl`	Discovering causal structures in binary exclusive-or skew acyclic models CoRR Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
2012	J	`jnl`	Discovery of non-gaussian linear causal models using ICA CoRR Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer
2012	—	`conf`	Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders. ICANN (1) Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio
2012	J	`jnl`	Joint estimation of linear non-Gaussian acyclic models. Neurocomputing Shohei Shimizu
2011	J	`jnl`	Analyzing relationships among ARMA processes based on non-Gaussianity of external influences. Neurocomputing Yoshinobu Kawahara, Shohei Shimizu, Takashi Washio
2011	J	`jnl`	DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model. J. Mach. Learn. Res. Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenneth Bollen
2011	A	`conf`	Discovering causal structures in binary exclusive-or skew acyclic models. UAI Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
2011	J	`jnl`	Estimating exogenous variables in data with more variables than observations. Neural Networks Yasuhiro Sogawa, Shohei Shimizu, Teppei Shimamura, Aapo Hyvärinen, Takashi Washio, Seiya Imoto
2010	B	`conf`	An experimental comparison of linear non-Gaussian causal discovery methods and their variants. IJCNN Yasuhiro Sogawa, Shohei Shimizu, Yoshinobu Kawahara, Takashi Washio
2010	—	`conf`	Assessing Statistical Reliability of LiNGAM via Multiscale Bootstrap. ICANN (3) Yusuke Komatsu, Shohei Shimizu, Hidetoshi Shimodaira
2010	—	`conf`	Discovery of Exogenous Variables in Data with More Variables Than Observations. ICANN (1) Yasuhiro Sogawa, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio, Teppei Shimamura, Seiya Imoto
2010	J	`jnl`	Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. J. Mach. Learn. Res. Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer
2010	J	`jnl`	GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables CoRR Yoshinobu Kawahara, Kenneth Bollen, Shohei Shimizu, Takashi Washio
2010	—	`conf`	Use of Prior Knowledge in a Non-Gaussian Method for Learning Linear Structural Equation Models. LVA/ICA Takanori Inazumi, Shohei Shimizu, Takashi Washio
2009	A	`conf`	A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. UAI Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara
2009	J	`jnl`	Estimation of linear non-Gaussian acyclic models for latent factors. Neurocomputing Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen
2008	A	`conf`	Causal discovery of linear acyclic models with arbitrary distributions. UAI Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
2008	A*	`conf`	Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity. ICML Aapo Hyvärinen, Shohei Shimizu, Patrik O. Hoyer
2008	J	`jnl`	Estimation of causal effects using linear non-Gaussian causal models with hidden variables. Int. J. Approx. Reason. Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen, Markus Palviainen
2007	—	`conf`	Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes. ICONIP (1) Shohei Shimizu, Aapo Hyvärinen
2006	J	`jnl`	A Linear Non-Gaussian Acyclic Model for Causal Discovery. J. Mach. Learn. Res. Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti J. Kerminen
2006	—	`conf`	A Quasi-stochastic Gradient Algorithm for Variance-Dependent Component Analysis. ICANN (2) Aapo Hyvärinen, Shohei Shimizu
2006	J	`jnl`	Estimation of linear, non-gaussian causal models in the presence of confounding latent variables CoRR Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
2006	—	`conf`	Estimation of linear, non-gaussian causal models in the presence of confounding latent variables. Probabilistic Graphical Models Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
2006	J	`jnl`	Finding a causal ordering via independent component analysis. Comput. Stat. Data Anal. Shohei Shimizu, Aapo Hyvärinen, Patrik O. Hoyer, Yutaka Kano
2006	—	`conf`	New Permutation Algorithms for Causal Discovery Using ICA. ICA Patrik O. Hoyer, Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Antti J. Kerminen
2006	—	`conf`	Testing Significance of Mixing and Demixing Coefficients in ICA. ICA Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer, Antti J. Kerminen
2005	A	`conf`	Discovery of Non-gaussian Linear Causal Models using ICA. UAI Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer