Jakob Runge - RankMe

55 papers A* 7A 6Journal 32Unranked 10

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Structural Causal Bottleneck Models. CoRR Simon Bing, Jonas Wahl, Jakob Runge
2025	J	`jnl`	Context-Specific Causal Graph Discovery with Unobserved Contexts: Non-Stationarity, Regimes and Spatio-Temporal Patterns. CoRR Martin Rabel, Jakob Runge
2025	J	`jnl`	Internal Incoherency Scores for Constraint-based Causal Discovery Algorithms. CoRR Sofia Faltenbacher, Jonas Wahl, Rebecca Herman, Jakob Runge
2025	J	`jnl`	Multidata Causal Discovery for Statistical Hurricane Intensity Forecasting. CoRR Saranya Ganesh S., Frederick Iat-Hin Tam, Milton S. Gomez, Marie McGraw, Mark DeMaria, Kate D. Musgrave, Jakob Runge, Tom Beucler
2025	—	`conf`	Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation. CLeaR Oana-Iuliana Popescu, Andreas Gerhardus, Martin Rabel, Jakob Runge
2025	A*	`conf`	Sanity Checking Causal Representation Learning on a Simple Real-World System. ICML Juan L. Gamella, Simon Bing, Jakob Runge
2025	J	`jnl`	Sanity Checking Causal Representation Learning on a Simple Real-World System. CoRR Juan L. Gamella, Simon Bing, Jakob Runge
2025	A	`conf`	Separation-Based Distance Measures for Causal Graphs. AISTATS Jonas Wahl, Jakob Runge
2025	—	`conf`	Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery. CLeaR Rebecca Herman, Jonas Wahl, Urmi Ninad, Jakob Runge
2025	J	`jnl`	Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery. CoRR Rebecca Herman, Jonas Wahl, Urmi Ninad, Jakob Runge
2025	J	`jnl`	Using Time Structure to Estimate Causal Effects. CoRR Tom Hochsprung, Jakob Runge, Andreas Gerhardus
2024	A	`conf`	A Global Markov Property for Solutions of Stochastic Difference Equations and the corresponding Full Time Graphs. UAI Tom Hochsprung, Jakob Runge, Andreas Gerhardus
2024	—	`conf`	Bootstrap aggregation and confidence measures to improve time series causal discovery. CLeaR Kevin Debeire, Andreas Gerhardus, Jakob Runge, Veronika Eyring
2024	J	`jnl`	Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support. CoRR Martin Rabel, Wiebke Günther, Jakob Runge, Andreas Gerhardus
2024	J	`jnl`	Causal Representation Learning in Temporal Data via Single-Parent Decoding. CoRR Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn, Yaniv Gurwicz, Dhanya Sridhar, Alexandre Drouin, Peer Nowack, Jakob Runge, David Rolnick
2024	A*	`conf`	Causal discovery with endogenous context variables. NeurIPS Wiebke Günther, Oana-Iuliana Popescu, Martin Rabel, Urmi Ninad, Andreas Gerhardus, Jakob Runge
2024	J	`jnl`	Causal discovery with endogenous context variables. CoRR Wiebke Günther, Oana-Iuliana Popescu, Martin Rabel, Urmi Ninad, Andreas Gerhardus, Jakob Runge
2024	—	`conf`	Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions. CLeaR Simon Bing, Urmi Ninad, Jonas Wahl, Jakob Runge
2024	J	`jnl`	Invariance & Causal Representation Learning: Prospects and Limitations. Trans. Mach. Learn. Res. Simon Bing, Tom Hochsprung, Jonas Wahl, Urmi Ninad, Jakob Runge
2023	J	`jnl`	A Causal Discovery Approach To Learn How Urban Form Shapes Sustainable Mobility Across Continents. CoRR Felix Wagner, Florian Nachtigall, Lukas Franken, Nikola Milojevic-Dupont, Rafael H. M. Pereira, Nicolas Koch, Jakob Runge, Marta Gonzalez, Felix Creutzig
2023	A	`conf`	Causal Discovery for time series from multiple datasets with latent contexts. UAI Wiebke Günther, Urmi Ninad, Jakob Runge
2023	A*	`conf`	ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning. NeurIPS Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh, Philippe Brouillard, Yaniv Gurwicz, Chandni Nagda, Jakob Runge, Peer Nowack, David Rolnick
2023	J	`jnl`	ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning. CoRR Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh, Philippe Brouillard, Yaniv Gurwicz, Chandni Nagda, Jakob Runge, Peer Nowack, David Rolnick
2023	J	`jnl`	Discovering Causal Relations and Equations from Data. CoRR Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge
2023	J	`jnl`	Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation. J. Mach. Learn. Res. Christoph Käding, Jakob Runge
2023	J	`jnl`	Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions. CoRR Simon Bing, Urmi Ninad, Jonas Wahl, Jakob Runge
2023	A	`conf`	Increasing effect sizes of pairwise conditional independence tests between random vectors. UAI Tom Hochsprung, Jonas Wahl, Andreas Gerhardus, Urmi Ninad, Jakob Runge
2023	J	`jnl`	Invariance & Causal Representation Learning: Prospects and Limitations. CoRR Simon Bing, Jonas Wahl, Urmi Ninad, Jakob Runge
2023	J	`jnl`	Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation. CoRR Oana-Iuliana Popescu, Andreas Gerhardus, Jakob Runge
2023	J	`jnl`	Projecting infinite time series graphs to finite marginal graphs using number theory. CoRR Andreas Gerhardus, Jonas Wahl, Sofia Faltenbacher, Urmi Ninad, Jakob Runge
2023	J	`jnl`	Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery. CoRR Saranya Ganesh S., Tom Beucler, Frederick Iat-Hin Tam, Milton S. Gomez, Jakob Runge, Andreas Gerhardus
2023	A*	`conf`	Vector Causal Inference between Two Groups of Variables. AAAI Jonas Wahl, Urmi Ninad, Jakob Runge
2022	A*	`conf`	Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery. NeurIPS Wiebke Günther, Urmi Ninad, Jonas Wahl, Jakob Runge
2021	J	`jnl`	A Data-Driven Approach to Partitioning Net Ecosystem Exchange Using a Deep State Space Model. IEEE Access Violeta Teodora Trifunov, Maha Shadaydeh, Jakob Runge, Markus Reichstein, Joachim Denzler
2021	—	`conf`	Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data. GCPR Christian Reimers, Paul Bodesheim, Jakob Runge, Joachim Denzler
2021	—	`conf`	Conditional Dependence Tests Reveal the Usage of ABCD Rule Features and Bias Variables in Automatic Skin Lesion Classification. CVPR Workshops Christian Reimers, Niklas Penzel, Paul Bodesheim, Jakob Runge, Joachim Denzler
2021	—	`conf`	EarthNet2021: A Large-Scale Dataset and Challenge for Earth Surface Forecasting as a Guided Video Prediction Task. CVPR Workshops Christian Requena-Mesa, Vitus Benson, Markus Reichstein, Jakob Runge, Joachim Denzler
2021	J	`jnl`	EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task. CoRR Christian Requena-Mesa, Vitus Benson, Markus Reichstein, Jakob Runge, Joachim Denzler
2021	J	`jnl`	Necessary and sufficient conditions for optimal adjustment sets in causal graphical models with hidden variables. CoRR Jakob Runge
2021	A*	`conf`	Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables. NeurIPS Jakob Runge
2021	J	`jnl`	Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing. CoRR Christian Reimers, Paul Bodesheim, Jakob Runge, Joachim Denzler
2020	J	`jnl`	A Perspective on Gaussian Processes for Earth Observation. CoRR Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge, Markus Reichstein
2020	—	`conf`	Determining the Relevance of Features for Deep Neural Networks. ECCV (26) Christian Reimers, Jakob Runge, Joachim Denzler
2020	A	`conf`	Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. UAI Jakob Runge
2020	J	`jnl`	Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. CoRR Jakob Runge
2020	J	`jnl`	EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts. CoRR Christian Requena-Mesa, Vitus Benson, Joachim Denzler, Jakob Runge, Markus Reichstein
2020	A*	`conf`	High-recall causal discovery for autocorrelated time series with latent confounders. NeurIPS Andreas Gerhardus, Jakob Runge
2020	J	`jnl`	High-recall causal discovery for autocorrelated time series with latent confounders. CoRR Andreas Gerhardus, Jakob Runge
2019	—	`conf`	Nonlinear Causal Link Estimation Under Hidden Confounding with an Application to Time Series Anomaly Detection. GCPR Violeta Teodora Trifunov, Maha Shadaydeh, Jakob Runge, Veronika Eyring, Markus Reichstein, Joachim Denzler
2019	—	`conf`	The Causality for Climate Competition. NeurIPS (Competition and Demos) Jakob Runge, Xavier-Andoni Tibau, Matthias Bruhns, Jordi Muñoz-Marí, Gustau Camps-Valls
2018	A	`conf`	Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. AISTATS Jakob Runge
2017	J	`jnl`	Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. CoRR Jakob Runge
2013	J	`jnl`	Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information. Entropy Jaroslav Hlinka, David Hartman, Martin Vejmelka, Jakob Runge, Norbert Marwan, Jürgen Kurths, Milan Palus
2013	J	`jnl`	Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System. Entropy Georgios Balasis, Reik V. Donner, Stelios M. Potirakis, Jakob Runge, Constantinos Papadimitriou, Ioannis A. Daglis, Konstantinos Eftaxias, Jürgen Kurths
2012	J	`jnl`	Quantifying Causal Coupling Strength: A Lag-specific Measure For Multivariate Time Series Related To Transfer Entropy CoRR Jakob Runge, Jobst Heitzig, Norbert Marwan, Jürgen Kurths