Alexander Marx



Department of Statistics

TU Dortmund


Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning

Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi

ICLR, 2024 May

Blood Glucose Forecasting from Temporal and Static Information in Children with T1D

Alexander Marx, Francesco Di Stefano, Heike Leutheuse, Kieran Chin-Cheong, Marc Pfister, Marie-Anne Burckhardt, Sara Bachmann Brenner, Julia E. Vogt

Frontiers in Pediatrics, vol. 11, 2023

Effective Bayesian Heteroscedastic Regression with Deep Neural Networks

Alexander Immer, Emanuele Palumbo, Alexander Marx*, Julia E Vogt*

NeurIPS, 2023 Dec

Beyond Normal: On the Evaluation of Mutual Information Estimators

Paweł Czyz, Frederic Grabowski, Julia E Vogt, Niko Beerenwinkel*, Alexander Marx*

NeurIPS, 2023 Dec

The Mixtures and the Neural Critics: On the Pointwise Mutual Information Profiles of Fine Distributions

Paweł Czyz, Frederic Grabowski, Julia E Vogt, Niko Beerenwinkel*, Alexander Marx*

2023 Oct

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

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

ICML, 2023 Jul

Identifiability Results for Multimodal Contrastive Learning

Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E Vogt

ICLR, 2023 May

3DIdentBox: A Toolbox for Identifiability Benchmarking

Alice Bizeul, Imant Daunhawer, Emanuele Palumbo, Bernhard Schölkopf, Alexander Marx, Julia E Vogt

CleaR (Dataset Track), 2023 Apr

Inferring Cause and Effect in the Presence of Heteroscedastic Noise

Sascha Xu, Osman Mian, Alexander Marx, Jilles Vreeken

ICML, 2022 Jul

Estimating Mutual Information via Geodesic kNN

Alexander Marx, Jonas Fischer

SIAM SDM, 2022 Apr

Formally Justifying MDL-based Inference of Cause and Effect

Alexander Marx, Jilles Vreeken

AAAI Workshop ITCI, 2022 Mar

Causal Inference with Heteroscedastic Noise Models

Sascha Xu, Alexander Marx, Osman Mian, Jilles Vreeken

AAAI Workshop ITCI, 2022 Mar

A Weaker Faithfulness Assumption based on Triple Interactions

Alexander Marx, Arthur Gretton, Joris M. Mooij

UAI, 2021

Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multidimensional Adaptive Histograms

Alexander Marx, Lincen Yang, Matthijs van Leeuwen

SIAM SDM, 2021, pp. 387--395

Discovering Fully Oriented Causal Networks

Osman Mian, Alexander Marx, Jilles Vreeken

AAAI, 2021

Integrative analysis of epigenetics data identifies gene-specific regulatory elements

Florian Schmidt, Alexander Marx, Nina Baumgarten, Marie Hebel, Martin Wegner, Manuel Kaulich, Matthias S Leisegang, Ralf P Brandes, Jonathan Göke, Jilles Vreeken, Marcel H Schulz

Nucleic Acids Research, 2021 Sep

Information-Theoretic Causal Discovery

Alexander Marx

Saarländische Universitäts-und Landesbibliothek, 2021 Jul

Identifiability of Cause and Effect using Regularized Regression

Alexander Marx, Jilles Vreeken

KDD, 2019

Telling cause from effect by local and global regression

Alexander Marx, Jilles Vreeken

Knowledge and Information Systems, 2019

Approximating Algorithmic Conditional Independence for Discrete Data

Alexander Marx, Jilles Vreeken

AAAI Symposium WHY, 2019

Stochastic Complexity for Testing Conditional Independence on Discrete Data

Alexander Marx, Jilles Vreeken

NeurIPS Workshop on Causal Learning, 2018

Causal Inference on Multivariate and Mixed-Type Data

Alexander Marx, Jilles Vreeken


EDISON-WMW: Exact Dynamic Programming Solution of the Wilcoxon-Mann-Whitney Test

Alexander Marx, Christina Backes, Eckart Meese, Hans-Peter Lenhof, Andreas Keller

Genomics, Proteomics & Bioinformatics, vol. 14, Elsevier, 2016, pp. 55--61


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