Publications

See Google Scholar or Research Gate for a full list of Jakob Runge’s publications. Selected recent publications of the Climate Informatics Group:

Reimers, C., J. Runge, and J. Denzler. “Determining the Relevance of Features for Deep Neural Networks” European Conference on Computer Vision ECCV (2020, accepted)

Runge, J. “Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets” Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI (2020).
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Nowack, P., J. Runge, V. Eyring, J.D. Haigh, “Causal networks for climate model evaluation and constrained projections”. Nature Communications 11: 1415 (2020).
Paper

Runge, J., P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, “Detecting and quantifying causal associations in large nonlinear time series datasets”. Science Advances 5, eaau4996 (2019).
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Runge, J., S. Bathiany, E. Bollt, G. Camps-Valls, D. Coumou, E. Deyle, C. Glymour, et al. “Inferring Causation from Time Series in Earth System Sciences.” Nature Communications 10: 2553 (2019).
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VT Trifunov, M Shadaydeh, J Runge, V Eyring, M Reichstein, J Denzler. Nonlinear causal link estimation under hidden confounding with an application to time series anomaly detection. German Conference on Pattern Recognition, 261-273 (2019)

Tibau, X., C. Requena-Mesa, C. Reimers, J. Denzler, V. Eyring, M. Reichstein, and J. Runge. 2018. “SupernoVAE : VAE Based Kernel PCA for Analysis of Spatio-Temporal Earth Data.” in Proceedings of the 8th International Workshop on Climate Informatics (2018), 1–4.

Runge, J. Causal network reconstruction from time series: From theoretical assumptions to practical estimation. Chaos An Interdiscip. J. Nonlinear Sci. 28, 075310 (2018).
Paper

Runge, J. Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. in Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (2018).
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