Alexander Marx


Postdoc Fellow


alexander.marx(at)inf.ethz.ch


ETH AI Center


ETH Zürich



Alexander Marx


Postdoc Fellow


Contact

Alexander Marx


Postdoc Fellow


alexander.marx(at)inf.ethz.ch


ETH AI Center


ETH Zürich




I am a Postdoc Fellow affiliated with the ETH AI Center and the Medical Data Science group at ETH Zürich. My research focuses on causality, information theory, and representation learning to approach modern challenges in the medical and biological domain. I obtained my Master’s degree in Bioinformatics in 2016 and completed my PhD in Computer Science in 2021 at Saarland University. During my PhD, I was part of the Exploratory Data Analysis group affiliated with the CISPA Helmholtz Center for Information Security and the Max Planck Institute for Informatics.

For Students: If you are looking for a thesis/semester project, feel free to contact me. Currently, my main focus is on representation learning from multimodal or time series data, which may include applications to the medical domain [1,2], and causality—especially considering heteroscedastic noise in causal models [3,4].

News

Activities

Reviewing for Conferences & Journals

  • Reviewer for the International Conference on Machine Learning (ICML'22)
  • Reviewer for the International Conference on Artificial Intelligence and Statistics (AISTATS'21,22 [Top 10% Reviewer Award],23[Top 10% Reviewer Award])
  • PC Member for the SIAM International Conference on Data Mining (SDM'21)
  • PC Member for the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'21)
  • Reviewer for the Machine Learning for Health Symposium (ML4H'22)
  • Reviewed for the journal Data Mining and Knowledge Discovery (DAMI'22)
  • Reviewed for the journal IEEE Access in 2020
  • Reviewer for the IEEE International Symposium on Information Theory (ISIT'19,20)
  • PC Member for the workshops AAAI-ITCI'22 and NeurIPS-TS4H

Invited Talks

Publications


On the Identifiability and Estimation of Causal Location-Scale Noise Models


Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx


arXiv, 2022 Oct


Inferring Cause and Effect in the Presence of Heteroscedastic Noise


Sascha Xu, Osman Mian, Alexander Marx, Jilles Vreeken


Proceedings of the International Conference on Machine Learning, PMLR, 2022 Jul


Estimating Mutual Information via Geodesic kNN


Alexander Marx, Jonas Fischer


Proceedings of the SIAM International Conference on Data Mining, SIAM, 2022 Apr


Formally Justifying MDL-based Inference of Cause and Effect


Alexander Marx, Jilles Vreeken


Proceedings of the AAAI Workshop on Information Theoretic Causal Inference and Discovery, 2022 Mar


Causal Inference with Heteroscedastic Noise Models


Sascha Xu, Alexander Marx, Osman Mian, Jilles Vreeken


Proceedings of the AAAI Workshop on Information Theoretic Causal Inference and Discovery, 2022 Mar


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