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2020 – today
- 2024
[c17]Simon Bing, Urmi Ninad, Jonas Wahl, Jakob Runge:
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions. CLeaR 2024: 843-867- [c16]Kevin Debeire, Andreas Gerhardus, Jakob Runge, Veronika Eyring:
Bootstrap aggregation and confidence measures to improve time series causal discovery. CLeaR 2024: 979-1007 - 2023
- [j4]Christoph Käding, Jakob Runge:
Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation. J. Mach. Learn. Res. 24: 278:1-278:144 (2023) - [c15]Jonas Wahl, Urmi Ninad, Jakob Runge:
Vector Causal Inference between Two Groups of Variables. AAAI 2023: 12305-12312 - [c14]Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh, Philippe Brouillard, Yaniv Gurwicz, Chandni Nagda, Jakob Runge, Peer Nowack, David Rolnick:
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning. NeurIPS 2023 - [c13]Wiebke Günther, Urmi Ninad, Jakob Runge:
Causal Discovery for time series from multiple datasets with latent contexts. UAI 2023: 766-776 - [c12]Tom Hochsprung, Jonas Wahl, Andreas Gerhardus, Urmi Ninad, Jakob Runge:
Increasing effect sizes of pairwise conditional independence tests between random vectors. UAI 2023: 879-889 - [i17]Saranya Ganesh S., Tom Beucler, Frederick Iat-Hin Tam, Milton S. Gomez, Jakob Runge, Andreas Gerhardus:
Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery. CoRR abs/2304.05294 (2023) - [i16]Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge:
Discovering Causal Relations and Equations from Data. CoRR abs/2305.13341 (2023) - [i15]Felix Wagner, Florian Nachtigall, Lukas Franken, Nikola Milojevic-Dupont, Rafael H. M. Pereira, Nicolas Koch, Jakob Runge, Marta Gonzalez, Felix Creutzig:
A Causal Discovery Approach To Learn How Urban Form Shapes Sustainable Mobility Across Continents. CoRR abs/2308.16599 (2023) - [i14]Andreas Gerhardus, Jonas Wahl, Sofia Faltenbacher, Urmi Ninad, Jakob Runge:
Projecting infinite time series graphs to finite marginal graphs using number theory. CoRR abs/2310.05526 (2023) - [i13]Oana-Iuliana Popescu, Andreas Gerhardus, Jakob Runge:
Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation. CoRR abs/2310.11132 (2023) - [i12]Simon Bing, Urmi Ninad, Jonas Wahl, Jakob Runge:
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions. CoRR abs/2311.02695 (2023) - [i11]Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh, Philippe Brouillard, Yaniv Gurwicz, Chandni Nagda, Jakob Runge, Peer Nowack, David Rolnick:
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning. CoRR abs/2311.03721 (2023) - [i10]Simon Bing, Jonas Wahl, Urmi Ninad, Jakob Runge:
Invariance & Causal Representation Learning: Prospects and Limitations. CoRR abs/2312.03580 (2023) - 2022
- [c11]Wiebke Günther, Urmi Ninad, Jonas Wahl, Jakob Runge:
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery. NeurIPS 2022 - 2021
- [j3]Violeta Teodora Trifunov
, Maha Shadaydeh, Jakob Runge, Markus Reichstein, Joachim Denzler:
A Data-Driven Approach to Partitioning Net Ecosystem Exchange Using a Deep State Space Model. IEEE Access 9: 107873-107883 (2021) - [c10]Christian Requena-Mesa, Vitus Benson, Markus Reichstein, Jakob Runge, Joachim Denzler:
EarthNet2021: A Large-Scale Dataset and Challenge for Earth Surface Forecasting as a Guided Video Prediction Task. CVPR Workshops 2021: 1132-1142 - [c9]Christian Reimers, Niklas Penzel, Paul Bodesheim, Jakob Runge, Joachim Denzler:
Conditional Dependence Tests Reveal the Usage of ABCD Rule Features and Bias Variables in Automatic Skin Lesion Classification. CVPR Workshops 2021: 1810-1819 - [c8]Christian Reimers, Paul Bodesheim, Jakob Runge, Joachim Denzler:
Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data. GCPR 2021: 48-62 - [c7]Jakob Runge:
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables. NeurIPS 2021: 15762-15773 - [i9]Jakob Runge:
Necessary and sufficient conditions for optimal adjustment sets in causal graphical models with hidden variables. CoRR abs/2102.10324 (2021) - [i8]Christian Reimers, Paul Bodesheim, Jakob Runge, Joachim Denzler:
Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing. CoRR abs/2103.06179 (2021) - [i7]Christian Requena-Mesa, Vitus Benson, Markus Reichstein, Jakob Runge, Joachim Denzler:
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task. CoRR abs/2104.10066 (2021) - 2020
- [c6]Christian Reimers, Jakob Runge, Joachim Denzler:
Determining the Relevance of Features for Deep Neural Networks. ECCV (26) 2020: 330-346 - [c5]Andreas Gerhardus, Jakob Runge:
High-recall causal discovery for autocorrelated time series with latent confounders. NeurIPS 2020 - [c4]Jakob Runge:
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. UAI 2020: 1388-1397 - [i6]Jakob Runge:
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. CoRR abs/2003.03685 (2020) - [i5]Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge, Markus Reichstein:
A Perspective on Gaussian Processes for Earth Observation. CoRR abs/2007.01238 (2020) - [i4]Andreas Gerhardus, Jakob Runge:
High-recall causal discovery for autocorrelated time series with latent confounders. CoRR abs/2007.01884 (2020) - [i3]Christian Requena-Mesa, Vitus Benson, Joachim Denzler, Jakob Runge, Markus Reichstein:
EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts. CoRR abs/2012.06246 (2020)
2010 – 2019
- 2019
- [c3]Violeta Teodora Trifunov, Maha Shadaydeh, Jakob Runge, Veronika Eyring, Markus Reichstein, Joachim Denzler:
Nonlinear Causal Link Estimation Under Hidden Confounding with an Application to Time Series Anomaly Detection. GCPR 2019: 261-273 - [c2]Jakob Runge, Xavier-Andoni Tibau, Matthias Bruhns, Jordi Muñoz-Marí, Gustau Camps-Valls:
The Causality for Climate Competition. NeurIPS (Competition and Demos) 2019: 110-120 - 2018
- [c1]Jakob Runge:
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. AISTATS 2018: 938-947 - 2017
- [i2]Jakob Runge:
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. CoRR abs/1709.01447 (2017) - 2013
- [j2]Jaroslav Hlinka, David Hartman, Martin Vejmelka, Jakob Runge, Norbert Marwan, Jürgen Kurths, Milan Palus:
Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information. Entropy 15(6): 2023-2045 (2013) - [j1]Georgios Balasis, Reik V. Donner, Stelios M. Potirakis, Jakob Runge, Constantinos Papadimitriou, Ioannis A. Daglis, Konstantinos Eftaxias, Jürgen Kurths:
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System. Entropy 15(11): 4844-4888 (2013) - 2012
- [i1]Jakob Runge, Jobst Heitzig, Norbert Marwan, Jürgen Kurths:
Quantifying Causal Coupling Strength: A Lag-specific Measure For Multivariate Time Series Related To Transfer Entropy. CoRR abs/1210.2748 (2012)
Coauthor Index
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last updated on 2024-09-14 02:03 CEST by the dblp team
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