default search action
Shohei Shimizu
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [j27]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. J. Mach. Learn. Res. 25: 60:1-60:8 (2024) - [c41]Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le:
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models. CLeaR 2024: 1118-1140 - [c40]Daisuke Takahashi, Shohei Shimizu, Takuma Tanaka:
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating. IJCNN 2024: 1-8 - [c39]Keito Inoshita, Xiaokang Zhou, Shohei Shimizu:
Multi-Domain and Multi-View Oriented Deep Neural Network for Sentiment Analysis in Large Language Models. iThings/GreenCom/CPSCom/SmartData/Cybermatics 2024: 149-156 - [i18]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 abs/2401.07231 (2024) - [i17]Masayuki Takayama, Tadahisa Okuda
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach. CoRR abs/2402.01454 (2024) - [i16]Daisuke Takahashi, Shohei Shimizu
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating. CoRR abs/2402.02678 (2024) - 2023
- [j26]Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu:
Python package for causal discovery based on LiNGAM. J. Mach. Learn. Res. 24: 14:1-14:8 (2023) - [j25]Xiaokang Zhou
Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networks. IEEE J. Sel. Areas Commun. 41(10): 3191-3211 (2023) - [j24]Xiaokang Zhou
Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications. IEEE Trans. Comput. Soc. Syst. 10(4): 1742-1751 (2023) - [j23]Yan Zeng
Nonlinear Causal Discovery for High-Dimensional Deterministic Data. IEEE Trans. Neural Networks Learn. Syst. 34(5): 2234-2245 (2023) - [c38]Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu:
Prospects of Continual Causality for Industrial Applications. AAAI Bridge Program 2023: 18-24 - [c37]Yi Jiang, Shohei Shimizu:
Linkages among the Foreign Exchange, Stock, and Bond Markets in Japan and the United States. CAWS 2023: 1-19 - [c36]Genta Kikuchi, Shohei Shimizu:
Structure Learning for Groups of Variables in Nonlinear Time-Series Data with Location-Scale Noise. CAWS 2023: 20-39 - [c35]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: 880-894 - [c34]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: 1-2 - [c33]Shusuke Wani, Xiaokang Zhou, Shohei Shimizu
BiLSTM and VAE Enhanced Multi-Task Neural Network for Trust-Aware E-Commerce Product Analysis. TrustCom 2023: 780-787 - [e1]Thuc Duy Le, Jiuyong Li, Robert Ness, Sofia Triantafillou, Shohei Shimizu, Peng Cui, Kun Kuang, Jian Pei, Fei Wang, Mattia Prosperi:
The KDD'23 Workshop on Causal Discovery, Prediction and Decision, 07 August 2023, Long Beach, CA, USA. Proceedings of Machine Learning Research 218, PMLR 2023 [contents] - [i15]Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu
Causal-learn: Causal Discovery in Python. CoRR abs/2307.16405 (2023) - [i14]Thong Pham, Shohei Shimizu
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models. CoRR abs/2311.00927 (2023) - 2022
- [j22]Takashi Nicholas Maeda
Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. Int. J. Data Sci. Anal. 13(2): 77-89 (2022) - [j21]Xiaokang Zhou
Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System. IEEE Internet Things J. 9(12): 9310-9319 (2022) - [j20]Gao Liu, Huidong Dong, Zheng Yan
B4SDC: A Blockchain System for Security Data Collection in MANETs. IEEE Trans. Big Data 8(3): 739-752 (2022) - [j19]Angtai Li, Yu Chen, Zheng Yan
A Survey on Integrity Auditing for Data Storage in the Cloud: From Single Copy to Multiple Replicas. IEEE Trans. Big Data 8(5): 1428-1442 (2022) - [j18]Xiaokang Zhou
Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems. IEEE Trans. Ind. Informatics 18(2): 1377-1386 (2022) - [c32]Kento Uemura, Takuya Takagi, Kambayashi Takayuki, Hiroyuki Yoshida, Shohei Shimizu:
A Multivariate Causal Discovery based on Post-Nonlinear Model. CLeaR 2022: 826-839 - [c31]Yan Zeng, Shohei Shimizu, Hidetoshi Matsui, Fuchun Sun:
Causal Discovery for Linear Mixed Data. CLeaR 2022: 994-1009 - [c30]Kazuhi Honjo, Xiaokang Zhou, Shohei Shimizu
CNN-GRU Based Deep Learning Model for Demand Forecast in Retail Industry. IJCNN 2022: 1-8 - 2021
- [j17]Li Peng, Wei Feng, Zheng Yan
Privacy preservation in permissionless blockchain: A survey. Digit. Commun. Networks 7(3): 295-307 (2021) - [j16]Xiaokang Zhou
Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems. IEEE Trans. Ind. Informatics 17(8): 5790-5798 (2021) - [c29]Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao:
Causal Discovery with Multi-Domain LiNGAM for Latent Factors. CAWS 2021: 1-4 - [c28]Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao:
Causal Discovery with Multi-Domain LiNGAM for Latent Factors. IJCAI 2021: 2097-2103 - [c27]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: 55-77 - [c26]Takashi Nicholas Maeda, Shohei Shimizu:
Causal additive models with unobserved variables. UAI 2021: 97-106 - [i13]Takashi Nicholas Maeda, Shohei Shimizu:
Discovery of Causal Additive Models in the Presence of Unobserved Variables. CoRR abs/2106.02234 (2021) - 2020
- [c25]Takashi Nicholas Maeda, Shohei Shimizu:
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. AISTATS 2020: 735-745 - [c24]Kento Uemura, Shohei Shimizu
Estimation of Post-Nonlinear Causal Models Using Autoencoding Structure. ICASSP 2020: 3312-3316 - [i12]Takashi Nicholas Maeda, Shohei Shimizu:
Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. CoRR abs/2001.04197 (2020) - [i11]Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao:
Causal Discovery with Multi-Domain LiNGAM for Latent Factors. CoRR abs/2009.09176 (2020)
2010 – 2019
- 2019
- [j15]Weimin Li
Personalization Recommendation Algorithm Based on Trust Correlation Degree and Matrix Factorization. IEEE Access 7: 45451-45459 (2019) - [j14]Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu
Analysis of cause-effect inference by comparing regression errors. PeerJ Comput. Sci. 5: e169 (2019) - [j13]Xiaokang Zhou
Multi-Modality Behavioral Influence Analysis for Personalized Recommendations in Health Social Media Environment. IEEE Trans. Comput. Soc. Syst. 6(5): 888-897 (2019) - 2018
- [c23]Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf:
Cause-Effect Inference by Comparing Regression Errors. AISTATS 2018: 900-909 - [c22]Weimin Li, Heng Zhu, Xiaokang Zhou, Shohei Shimizu
A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree. DASC/PiCom/DataCom/CyberSciTech 2018: 418-422 - [i10]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 abs/1802.05889 (2018) - [i9]Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf:
Analysis of Cause-Effect Inference via Regression Errors. CoRR abs/1802.06698 (2018) - 2017
- [j12]Ricardo Silva, Shohei Shimizu:
Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions. J. Mach. Learn. Res. 18: 120:1-120:49 (2017) - [c21]Patrick Blöbaum, Shohei Shimizu, Takashi Washio:
A novel principle for causal inference in data with small error variance. ESANN 2017 - [c20]Patrick Blöbaum, Shohei Shimizu
Estimation of interventional effects of features on prediction. MLSP 2017: 1-6 - 2016
- [i8]Patrick Blöbaum, Takashi Washio, Shohei Shimizu:
Error Asymmetry in Causal and Anticausal Regression. CoRR abs/1610.03263 (2016) - 2015
- [c19]Patrick Blöbaum, Shohei Shimizu
Discriminative and Generative Models in Causal and Anticausal Settings. AMBN@JSAI-isAI 2015: 209-221 - [c18]Shohei Shimizu
A Non-Gaussian Approach for Causal Discovery in the Presence of Hidden Common Causes. AMBN@JSAI-isAI 2015: 222-233 - 2014
- [j11]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): 2629-2652 (2014) - [j10]Tatsuya Tashiro, Shohei Shimizu
ParceLiNGAM: A Causal Ordering Method Robust Against Latent Confounders. Neural Comput. 26(1): 57-83 (2014) - [i7]Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara:
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM. CoRR abs/1401.5636 (2014) - [i6]Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara:
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. CoRR abs/1408.2038 (2014) - 2013
- [c17]Kento Kadowaki, Shohei Shimizu
Estimation of causal structures in longitudinal data using non-Gaussianity. MLSP 2013: 1-6 - 2012
- [j9]Shohei Shimizu
Joint estimation of linear non-Gaussian acyclic models. Neurocomputing 81: 104-107 (2012) - [c16]Tatsuya Tashiro, Shohei Shimizu
Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders. ICANN (1) 2012: 491-498 - [c15]Kittitat Thamvitayakul, Shohei Shimizu
Bootstrap Confidence Intervals in DirectLiNGAM. ICDM Workshops 2012: 659-668 - [i5]Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara:
Discovering causal structures in binary exclusive-or skew acyclic models. CoRR abs/1202.3736 (2012) - [i4]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 abs/1206.3260 (2012) - [i3]Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer:
Discovery of non-gaussian linear causal models using ICA. CoRR abs/1207.1413 (2012) - 2011
- [j8]Yoshinobu Kawahara
Analyzing relationships among ARMA processes based on non-Gaussianity of external influences. Neurocomputing 74(12-13): 2212-2221 (2011) - [j7]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: 1225-1248 (2011) - [j6]Yasuhiro Sogawa, Shohei Shimizu
Estimating exogenous variables in data with more variables than observations. Neural Networks 24(8): 875-880 (2011) - [c14]Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara:
Discovering causal structures in binary exclusive-or skew acyclic models. UAI 2011: 373-382 - 2010
- [j5]Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer:
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. J. Mach. Learn. Res. 11: 1709-1731 (2010) - [c13]Takanori Inazumi, Shohei Shimizu
Use of Prior Knowledge in a Non-Gaussian Method for Learning Linear Structural Equation Models. LVA/ICA 2010: 221-228 - [c12]Yasuhiro Sogawa, Shohei Shimizu
Discovery of Exogenous Variables in Data with More Variables Than Observations. ICANN (1) 2010: 67-76 - [c11]Yusuke Komatsu, Shohei Shimizu
Assessing Statistical Reliability of LiNGAM via Multiscale Bootstrap. ICANN (3) 2010: 309-314 - [c10]Yasuhiro Sogawa, Shohei Shimizu
An experimental comparison of linear non-Gaussian causal discovery methods and their variants. IJCNN 2010: 1-8 - [i2]Yoshinobu Kawahara, Kenneth Bollen, Shohei Shimizu, Takashi Washio:
GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables. CoRR abs/1006.5041 (2010)
2000 – 2009
- 2009
- [j4]Shohei Shimizu
Estimation of linear non-Gaussian acyclic models for latent factors. Neurocomputing 72(7-9): 2024-2027 (2009) - [c9]Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara:
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. UAI 2009: 506-513 - 2008
- [j3]Patrik O. Hoyer, Shohei Shimizu
Estimation of causal effects using linear non-Gaussian causal models with hidden variables. Int. J. Approx. Reason. 49(2): 362-378 (2008) - [c8]Aapo Hyvärinen, Shohei Shimizu, Patrik O. Hoyer:
Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity. ICML 2008: 424-431 - [c7]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: 282-289 - 2007
- [c6]Shohei Shimizu
Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes. ICONIP (1) 2007: 752-761 - 2006
- [j2]Shohei Shimizu
Finding a causal ordering via independent component analysis. Comput. Stat. Data Anal. 50(11): 3278-3293 (2006) - [j1]Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti J. Kerminen:
A Linear Non-Gaussian Acyclic Model for Causal Discovery. J. Mach. Learn. Res. 7: 2003-2030 (2006) - [c5]Patrik O. Hoyer, Shohei Shimizu
New Permutation Algorithms for Causal Discovery Using ICA. ICA 2006: 115-122 - [c4]Shohei Shimizu
Testing Significance of Mixing and Demixing Coefficients in ICA. ICA 2006: 901-908 - [c3]Aapo Hyvärinen, Shohei Shimizu
A Quasi-stochastic Gradient Algorithm for Variance-Dependent Component Analysis. ICANN (2) 2006: 211-220 - [c2]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: 155-162 - [i1]Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen:
Estimation of linear, non-gaussian causal models in the presence of confounding latent variables. CoRR abs/cs/0603038 (2006) - 2005
- [c1]Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer:
Discovery of Non-gaussian Linear Causal Models using ICA. UAI 2005: 525-533
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-11 22:27 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: