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ORCIDAlexander Marx 0001
Person information
- affiliation: TU Dortmund University, Dortmund, Germany
- affiliation (former): CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
- affiliation (former): Chair for Clinical Bioinformatics, Medical Faculty, Saarland University, Saarbrücken, Germany
Other persons with the same name
- Alexander Marx 0002
![[0009-0001-9216-1420]](https://devdomain4test.ir/img/orcid-mark.12x12.png "0009-0001-9216-1420")
— Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Berlin, Germany - Alexander Marx 0003 — Heidelberg University Hospital, Heidelberg, Germany
- Alexander Marx 0005 — Planet Labs Germany GmbH, Berlin, Germany
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Books and Theses
- 2021
[b1]Alexander Marx:
Information-Theoretic Causal Discovery. Saarland University, Saarbrücken, Germany, 2021
Journal Articles
- 2026
- [j5]Marlies Hafer, Alexander Marx:
Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding. Mach. Learn. 115(5): 115 (2026) - 2025
- [j4]Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx:
On the Properties and Estimation of Pointwise Mutual Information Profiles. Trans. Mach. Learn. Res. 2025 (2025) - [j3]Alain Ryser, Thomas M. Sutter, Alexander Marx, Julia E. Vogt:
Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection. Trans. Mach. Learn. Res. 2025 (2025) - 2019
- [j2]Alexander Marx, Jilles Vreeken:
Telling cause from effect by local and global regression. Knowl. Inf. Syst. 60(3): 1277-1305 (2019) - 2016
- [j1]Alexander Marx, Christina Backes, Eckart Meese, Hans-Peter Lenhof, Andreas Keller:
EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon-Mann-Whitney Test. Genom. Proteom. Bioinform. 14(1): 55-61 (2016)
Conference and Workshop Papers
- 2025
- [c16]Ina Dormuth, Sven Franke, Marlies Hafer, Tim Katzke, Alexander Marx, Emmanuel Müller, Daniel Neider, Markus Pauly, Jérôme Rutinowski:
A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany. xAI (5) 2025: 64-85 - 2024
- [c15]Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi:
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning. ICLR 2024 - [c14]Alain Ryser, Chuhao Feng, Tobias Scheithauer, Marc Pfister, Marie-Anne Burckhardt, Sara Bachmann, Alexander Marx, Julia E. Vogt:
Transfer Learning for Pediatric Glucose Forecasting. ML4H@NeurIPS 2024: 839-860 - 2023
- [c13]Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt:
Identifiability Results for Multimodal Contrastive Learning. ICLR 2023 - [c12]Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx:
On the Identifiability and Estimation of Causal Location-Scale Noise Models. ICML 2023: 14316-14332 - [c11]Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx:
Beyond Normal: On the Evaluation of Mutual Information Estimators. NeurIPS 2023 - [c10]Alexander Immer, Emanuele Palumbo, Alexander Marx, Julia E. Vogt:
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks. NeurIPS 2023 - 2022
- [c9]Sascha Xu, Osman Mian, Alexander Marx, Jilles Vreeken:
Inferring Cause and Effect in the Presence of Heteroscedastic Noise. ICML 2022: 24615-24630 - [c8]Alexander Marx, Jonas Fischer:
Estimating Mutual Information via Geodesic kNN. SDM 2022: 415-423 - 2021
- [c7]Osman Mian, Alexander Marx, Jilles Vreeken:
Discovering Fully Oriented Causal Networks. AAAI 2021: 8975-8982 - [c6]Alexander Marx, Lincen Yang, Matthijs van Leeuwen:
Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. SDM 2021: 387-395 - [c5]Alexander Marx, Arthur Gretton, Joris M. Mooij:
A weaker faithfulness assumption based on triple interactions. UAI 2021: 451-460 - 2019
- [c4]Alexander Marx, Jilles Vreeken:
Testing Conditional Independence on Discrete Data using Stochastic Complexity. AISTATS 2019: 496-505 - [c3]Alexander Marx, Jilles Vreeken:
Identifiability of Cause and Effect using Regularized Regression. KDD 2019: 852-861 - 2018
- [c2]Alexander Marx, Jilles Vreeken:
Causal Inference on Multivariate and Mixed-Type Data. ECML/PKDD (2) 2018: 655-671 - 2017
- [c1]Alexander Marx, Jilles Vreeken:
Telling Cause from Effect Using MDL-Based Local and Global Regression. ICDM 2017: 307-316
Informal and Other Publications
- 2025
- [i16]Ina Dormuth, Sven Franke, Marlies Hafer, Tim Katzke, Alexander Marx, Emmanuel Müller, Daniel Neider, Markus Pauly, Jérôme Rutinowski:
A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany. CoRR abs/2502.15568 (2025) - [i15]Marlies Hafer, Alexander Marx:
Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding. CoRR abs/2503.20546 (2025) - [i14]Daniel Klippert, Alexander Marx:
Skewness-Robust Causal Discovery in Location-Scale Noise Models. CoRR abs/2511.14441 (2025) - 2024
- [i13]Alain Ryser, Thomas M. Sutter, Alexander Marx, Julia E. Vogt:
Anomaly Detection by Context Contrasting. CoRR abs/2405.18848 (2024) - 2023
- [i12]Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt:
Identifiability Results for Multimodal Contrastive Learning. CoRR abs/2303.09166 (2023) - [i11]Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx:
Beyond Normal: On the Evaluation of Mutual Information Estimators. CoRR abs/2306.11078 (2023) - [i10]Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi:
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning. CoRR abs/2310.07518 (2023) - [i9]Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx:
The Mixtures and the Neural Critics: On the Pointwise Mutual Information Profiles of Fine Distributions. CoRR abs/2310.10240 (2023) - 2022
- [i8]Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx:
On the Identifiability and Estimation of Causal Location-Scale Noise Models. CoRR abs/2210.09054 (2022) - 2021
- [i7]Alexander Marx, Lincen Yang, Matthijs van Leeuwen:
Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. CoRR abs/2101.05009 (2021) - [i6]Alexander Marx, Jilles Vreeken:
Formally Justifying MDL-based Inference of Cause and Effect. CoRR abs/2105.01902 (2021) - [i5]Alexander Marx, Jonas Fischer:
Estimating Mutual Information via Geodesic kNN. CoRR abs/2110.13883 (2021) - 2020
- [i4]Alexander Marx, Arthur Gretton, Joris M. Mooij:
A Weaker Faithfulness Assumption based on Triple Interactions. CoRR abs/2010.14265 (2020) - 2019
- [i3]Alexander Marx, Jilles Vreeken:
Testing Conditional Independence on Discrete Data using Stochastic Complexity. CoRR abs/1903.04829 (2019) - 2018
- [i2]Alexander Marx, Jilles Vreeken:
Causal Discovery by Telling Apart Parents and Children. CoRR abs/1808.06356 (2018) - 2017
- [i1]Alexander Marx, Jilles Vreeken:
Causal Inference on Multivariate Mixed-Type Data by Minimum Description Length. CoRR abs/1702.06385 (2017)
Coauthor Index
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