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Nonlinear Causal Link Estimation Under Hidden Confounding with an Application to Time Series Anomaly Detection., , , , , and . GCPR, volume 11824 of Lecture Notes in Computer Science, page 261-273. Springer, (2019)Optimal model-free prediction from multivariate time series, , and . (2015)cite arxiv:1506.05822Comment: 14 pages, 9 figures.Determining the Relevance of Features for Deep Neural Networks., , and . ECCV (26), volume 12371 of Lecture Notes in Computer Science, page 330-346. Springer, (2020)A Data-Driven Approach to Partitioning Net Ecosystem Exchange Using a Deep State Space Model., , , , and . IEEE Access, (2021)ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning., , , , , , , , and . CoRR, (2023)A Causal Discovery Approach To Learn How Urban Form Shapes Sustainable Mobility Across Continents., , , , , , , , and . CoRR, (2023)High-recall causal discovery for autocorrelated time series with latent confounders., and . NeurIPS, (2020)Bootstrap aggregation and confidence measures to improve time series causal discovery., , , and . CLeaR, volume 236 of Proceedings of Machine Learning Research, page 979-1007. PMLR, (2024)Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions., , , and . CLeaR, volume 236 of Proceedings of Machine Learning Research, page 843-867. PMLR, (2024)Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information.. CoRR, (2017)