| Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders | 0 | 0.34 | 2022 |
| Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System | 6 | 0.42 | 2022 |
| Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems | 7 | 0.45 | 2021 |
| Causal Discovery with Multi-Domain LiNGAM for Latent Factors. | 0 | 0.34 | 2021 |
| Privacy Preservation In Permissionless Blockchain: A Survey | 2 | 0.44 | 2021 |
| B4SDC: A Blockchain System for Security Data Collection in MANETs | 1 | 0.35 | 2020 |
| Analysis of cause-effect inference by comparing regression errors. | 1 | 0.37 | 2019 |
| Multi-Modality Behavioral Influence Analysis for Personalized Recommendations in Health Social Media Environment | 9 | 0.46 | 2019 |
| Personalization Recommendation Algorithm Based On Trust Correlation Degree And Matrix Factorization | 1 | 0.35 | 2019 |
| A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree | 0 | 0.34 | 2018 |
| Analysis of Cause-Effect Inference via Regression Errors. | 0 | 0.34 | 2018 |
| A novel principle for causal inference in data with small error variance. | 2 | 0.39 | 2017 |
| Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions. | 0 | 0.34 | 2017 |
| Error Asymmetry in Causal and Anticausal Regression. | 4 | 0.56 | 2016 |
| Discriminative and Generative Models in Causal and Anticausal Settings. | 1 | 0.41 | 2015 |
| A Non-Gaussian Approach for Causal Discovery in the Presence of Hidden Common Causes. | 0 | 0.34 | 2015 |
| Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM. | 0 | 0.34 | 2014 |
| A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model | 9 | 0.81 | 2014 |
| ParceLiNGAM: a causal ordering method robust against latent confounders. | 1 | 0.36 | 2014 |
| Estimation of causal structures in longitudinal data using non-Gaussianity | 0 | 0.34 | 2013 |
| Causal discovery of linear acyclic models with arbitrary distributions | 19 | 1.34 | 2012 |
| Bootstrap Confidence Intervals in DirectLiNGAM | 0 | 0.34 | 2012 |
| Joint estimation of linear non-Gaussian acyclic models. | 3 | 0.45 | 2012 |
| Estimation of causal orders in a linear non-gaussian acyclic model: a method robust against latent confounders | 1 | 0.36 | 2012 |
| Discovering causal structures in binary exclusive-or skew acyclic models | 0 | 0.34 | 2012 |
| DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model | 49 | 2.09 | 2011 |
| Analyzing relationships among ARMA processes based on non-Gaussianity of external influences | 6 | 0.56 | 2011 |
| Estimating exogenous variables in data with more variables than observations. | 5 | 0.45 | 2011 |
| An Experimental Comparison Of Linear Non-Gaussian Causal Discovery Methods And Their Variants | 5 | 0.54 | 2010 |
| Assessing statistical reliability of LiNGAM via multiscale bootstrap | 1 | 0.36 | 2010 |
| Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity | 42 | 1.85 | 2010 |
| Discovery of exogenous variables in data with more variables than observations | 2 | 0.43 | 2010 |
| GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables | 6 | 0.64 | 2010 |
| Use of prior knowledge in a non-Gaussian method for learning linear structural equation models | 1 | 0.41 | 2010 |
| Estimation of linear non-Gaussian acyclic models for latent factors | 4 | 0.58 | 2009 |
| Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity | 23 | 1.36 | 2008 |
| Estimation of causal effects using linear non-Gaussian causal models with hidden variables | 35 | 2.09 | 2008 |
| Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes | 2 | 0.37 | 2007 |
| Estimation of linear, non-gaussian causal models in the presence of confounding latent variables | 8 | 2.18 | 2006 |
| Testing significance of mixing and demixing coefficients in ICA | 6 | 1.61 | 2006 |
| A Linear Non-Gaussian Acyclic Model for Causal Discovery | 207 | 13.36 | 2006 |
| Finding a causal ordering via independent component analysis | 7 | 2.22 | 2006 |
| A quasi-stochastic gradient algorithm for variance-dependent component analysis | 3 | 0.47 | 2006 |
| New permutation algorithms for causal discovery using ICA | 4 | 1.51 | 2006 |
| Discovery of non-gaussian linear causal models using ICA | 9 | 1.83 | 2005 |
