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Estimation of linear, non-gaussian causal models in the presence of confounding latent variables., , and . Probabilistic Graphical Models, page 155-162. (2006)Estimation of interventional effects of features on prediction., and . MLSP, page 1-6. IEEE, (2017)RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders., and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 735-745. PMLR, (2020)A Survey on Integrity Auditing for Data Storage in the Cloud: From Single Copy to Multiple Replicas., , , , and . IEEE Trans. Big Data, 8 (5): 1428-1442 (2022)Prospects of Continual Causality for Industrial Applications., , , , , and . AAAI Bridge Program, volume 208 of Proceedings of Machine Learning Research, page 18-24. PMLR, (2023)A Non-Gaussian Approach for Causal Discovery in the Presence of Hidden Common Causes.. AMBN@JSAI-isAI, volume 9505 of Lecture Notes in Computer Science, page 222-233. Springer, (2015)Structure Learning for Groups of Variables in Nonlinear Time-Series Data with Location-Scale Noise., and . CAWS, volume 223 of Proceedings of Machine Learning Research, page 20-39. PMLR, (2023)Testing Significance of Mixing and Demixing Coefficients in ICA., , , , and . ICA, volume 3889 of Lecture Notes in Computer Science, page 901-908. Springer, (2006)Estimating individual-level optimal causal interventions combining causal models and machine learning models., , , , , and . CD@KDD, volume 150 of Proceedings of Machine Learning Research, page 55-77. PMLR, (2021)Discovery of Exogenous Variables in Data with More Variables Than Observations., , , , , and . ICANN (1), volume 6352 of Lecture Notes in Computer Science, page 67-76. Springer, (2010)