Andreas Gerhardus

16 papers A* 2A 2C 1Journal 9Unranked 2

Year	Rank	Type	Title / Venue / Authors
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	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	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	C	`conf`	Leveraging Causal Information for Multivariate Timeseries Anomaly Detection. DX Lukas Heppel, Andreas Gerhardus, Ferdinand Rewicki, Jan Christian Deeken, Günther Waxenegger-Wilfing
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	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`	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
2021	J	`jnl`	Characterization of causal ancestral graphs for time series with latent confounders. CoRR Andreas Gerhardus
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