Jakob Runge

According to our database, Jakob Runge authored at least 35 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Causal discovery with endogenous context variables.

Wiebke Günther, Oana-Iuliana Popescu, Martin Rabel, Urmi Ninad, Andreas Gerhardus, Jakob Runge

CoRR, 2024

Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support.

Martin Rabel, Wiebke Günther, Jakob Runge, Andreas Gerhardus

CoRR, 2024

Causal Representation Learning in Temporal Data via Single-Parent Decoding.

Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn, Yaniv Gurwicz, Dhanya Sridhar, Alexandre Drouin, Peer Nowack, Jakob Runge, David Rolnick

CoRR, 2024

Bootstrap aggregation and confidence measures to improve time series causal discovery.

Kevin Debeire, Andreas Gerhardus, Jakob Runge, Veronika Eyring

Proceedings of the Causal Learning and Reasoning, 2024

Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions.

Simon Bing, Urmi Ninad, Jonas Wahl, Jakob Runge

Proceedings of the Causal Learning and Reasoning, 2024

2023

Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation.

Christoph Käding, Jakob Runge

J. Mach. Learn. Res., 2023

Invariance & Causal Representation Learning: Prospects and Limitations.

Simon Bing, Jonas Wahl, Urmi Ninad, Jakob Runge

CoRR, 2023

Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation.

Oana-Iuliana Popescu, Andreas Gerhardus, Jakob Runge

CoRR, 2023

Projecting infinite time series graphs to finite marginal graphs using number theory.

Andreas Gerhardus, Jonas Wahl, Sofia Faltenbacher, Urmi Ninad, Jakob Runge

CoRR, 2023

A Causal Discovery Approach To Learn How Urban Form Shapes Sustainable Mobility Across Continents.

Felix Wagner, Florian Nachtigall, Lukas Franken, Nikola Milojevic-Dupont, Rafael H. M. Pereira, Nicolas Koch, Jakob Runge, Marta Gonzalez, Felix Creutzig

CoRR, 2023

Discovering Causal Relations and Equations from Data.

Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge

CoRR, 2023

Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery.

Saranya Ganesh S., Tom Beucler, Frederick Iat-Hin Tam, Milton S. Gomez, Jakob Runge, Andreas Gerhardus

CoRR, 2023

Increasing effect sizes of pairwise conditional independence tests between random vectors.

Tom Hochsprung, Jonas Wahl, Andreas Gerhardus, Urmi Ninad, Jakob Runge

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Causal Discovery for time series from multiple datasets with latent contexts.

Wiebke Günther, Urmi Ninad, Jakob Runge

Proceedings of the Uncertainty in Artificial Intelligence, 2023

ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning.

Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh, Philippe Brouillard, Yaniv Gurwicz, Chandni Nagda, Jakob Runge, Peer Nowack, David Rolnick

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Vector Causal Inference between Two Groups of Variables.

Jonas Wahl, Urmi Ninad, Jakob Runge

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery.

Wiebke Günther, Urmi Ninad, Jonas Wahl, Jakob Runge

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing.

Christian Reimers, Paul Bodesheim, Jakob Runge, Joachim Denzler

CoRR, 2021

Necessary and sufficient conditions for optimal adjustment sets in causal graphical models with hidden variables.

Jakob Runge

CoRR, 2021

A Data-Driven Approach to Partitioning Net Ecosystem Exchange Using a Deep State Space Model.

Violeta Teodora Trifunov, Maha Shadaydeh, Jakob Runge, Markus Reichstein, Joachim Denzler

IEEE Access, 2021

Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables.

Jakob Runge

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data.

Christian Reimers, Paul Bodesheim, Jakob Runge, Joachim Denzler

Proceedings of the Pattern Recognition - 43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28, 2021

EarthNet2021: A Large-Scale Dataset and Challenge for Earth Surface Forecasting as a Guided Video Prediction Task.

Christian Requena-Mesa, Vitus Benson, Markus Reichstein, Jakob Runge, Joachim Denzler

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

Conditional Dependence Tests Reveal the Usage of ABCD Rule Features and Bias Variables in Automatic Skin Lesion Classification.

Christian Reimers, Niklas Penzel, Paul Bodesheim, Jakob Runge, Joachim Denzler

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020

EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts.

Christian Requena-Mesa, Vitus Benson, Joachim Denzler, Jakob Runge, Markus Reichstein

CoRR, 2020

A Perspective on Gaussian Processes for Earth Observation.

Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge, Markus Reichstein

CoRR, 2020

Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets.

Jakob Runge

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

High-recall causal discovery for autocorrelated time series with latent confounders.

Andreas Gerhardus, Jakob Runge

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Determining the Relevance of Features for Deep Neural Networks.

Christian Reimers, Jakob Runge, Joachim Denzler

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

The Causality for Climate Competition.

Jakob Runge, Xavier-Andoni Tibau, Matthias Bruhns, Jordi Muñoz-Marí, Gustau Camps-Valls

Proceedings of the NeurIPS 2019 Competition and Demonstration Track, 2019

Nonlinear Causal Link Estimation Under Hidden Confounding with an Application to Time Series Anomaly Detection.

Violeta Teodora Trifunov, Maha Shadaydeh, Jakob Runge, Veronika Eyring, Markus Reichstein, Joachim Denzler

Proceedings of the Pattern Recognition, 2019

2018

Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information.

Jakob Runge

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2013

Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information.

Jaroslav Hlinka, David Hartman, Martin Vejmelka, Jakob Runge, Norbert Marwan, Jürgen Kurths, Milan Palus

Entropy, 2013

Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System.

Georgios Balasis, Reik V. Donner, Stelios M. Potirakis, Jakob Runge, Constantinos Papadimitriou, Ioannis A. Daglis, Konstantinos Eftaxias, Jürgen Kurths

Entropy, 2013

2012

Quantifying Causal Coupling Strength: A Lag-specific Measure For Multivariate Time Series Related To Transfer Entropy

Jakob Runge, Jobst Heitzig, Norbert Marwan, Jürgen Kurths

CoRR, 2012