Alexander Marx
Alexander Marx
Postdoctoral Researcher, ETH Zurich
Verified email at - Homepage
Cited by
Cited by
EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test
A Marx, C Backes, E Meese, HP Lenhof, A Keller
Genomics, proteomics & bioinformatics 14 (1), 55-61, 2016
Telling Cause from Effect using MDL-based Local and Global Regression
A Marx, J Vreeken
2017 IEEE International Conference on Data Mining (ICDM), 307–316, 2017
Testing Conditional Independence on Discrete Data using Stochastic Complexity
A Marx, J Vreeken
The 22nd International Conference on Artificial Intelligence and Statistics, 2019
Discovering Fully Oriented Causal Networks
O Mian, A Marx, J Vreeken
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021
Identifiability of Cause and Effect using Regularized Regression
A Marx, J Vreeken
SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019
Causal Inference on Multivariate and Mixed-Type Data
A Marx, J Vreeken
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019
Integrative analysis of epigenetics data identifies gene-specific regulatory elements
F Schmidt, A Marx, N Baumgarten, M Hebel, M Wegner, M Kaulich, ...
Nucleic Acids Research 49 (18), 10397-10418, 2021
Estimating conditional mutual information for discrete-continuous mixtures using multi-dimensional adaptive histograms
A Marx, L Yang, M van Leeuwen
Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021
Telling cause from effect by local and global regression
A Marx, J Vreeken
Knowledge and Information Systems 60 (3), 1277-1305, 2019
A Weaker Faithfulness Assumption based on Triple Interactions
A Marx, A Gretton, JM Mooij
Conference on Uncertainty in Artificial Intelligence (UAI), 2021
On the identifiability and estimation of causal location-scale noise models
A Immer, C Schultheiss, JE Vogt, B Schölkopf, P Bühlmann, A Marx
International Conference on Machine Learning, 14316-14332, 2023
Formally Justifying MDL-based Inference of Cause and Effect
A Marx, J Vreeken
AAAI'22 Workshop on Information-Theoretic Methods for Causal Inference and …, 2022
Inferring Cause and Effect in the Presence of Heteroscedastic Noise
S Xu, OA Mian, A Marx, J Vreeken
International Conference on Machine Learning, 24615-24630, 2022
Identifiability Results for Multimodal Contrastive Learning
I Daunhawer, A Bizeul, E Palumbo, A Marx, JE Vogt
International Conference on Learning Representations, 2023
Causal Discovery by Telling Apart Parents and Children
A Marx, J Vreeken
arXiv preprint arXiv:1808.06356, 2018
Causal Inference with Heteroscedastic Noise Models
S Xu, A Marx, O Mian, J Vreeken
Proceedings of the AAAI Workshop on Information Theoretic Causal Inference …, 2022
Stochastic complexity for testing conditional independence on discrete data
A Marx, J Vreeken
NeurIPS 2018 Workshop on Causal Learning, 2018
Estimating Mutual Information via Geodesic kNN
A Marx, J Fischer
Proceedings of the 2022 SIAM International Conference on Data Mining (SDM …, 2022
Approximating Algorithmic Conditional Independence for Discrete Data
A Marx, J Vreeken
Proceedings of the First AAAI Spring Symposium Beyond Curve Fitting …, 2019
Beyond Normal: On the Evaluation of Mutual Information Estimators
P Czyż, F Grabowski, JE Vogt, N Beerenwinkel, A Marx
arXiv preprint arXiv:2306.11078, 2023
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