EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon-Mann-Whitney Test. - Alexander Marx 001; Christina Backes; Eckart Meese; Hans-Peter Lenhof; Andreas Keller (Genom. Proteom. Bioinform., 2016)

Telling Cause from Effect Using MDL-Based Local and Global Regression. - Alexander Marx 001; Jilles Vreeken (ICDM, 2017)

Causal Inference on Multivariate Mixed-Type Data by Minimum Description Length. - Alexander Marx 001; Jilles Vreeken (CoRR, 2017)

Causal Inference on Multivariate and Mixed-Type Data. - Alexander Marx 001; Jilles Vreeken (ECML/PKDD (2), 2018)

Causal Discovery by Telling Apart Parents and Children. - Alexander Marx 001; Jilles Vreeken (CoRR, 2018)

Testing Conditional Independence on Discrete Data using Stochastic Complexity. - Alexander Marx 001; Jilles Vreeken (AISTATS, 2019)

Identifiability of Cause and Effect using Regularized Regression. - Alexander Marx 001; Jilles Vreeken (KDD, 2019)

Telling cause from effect by local and global regression. - Alexander Marx 001; Jilles Vreeken (Knowl. Inf. Syst., 2019)

Discovering Fully Oriented Causal Networks. - Osman Mian; Alexander Marx 001; Jilles Vreeken (AAAI, 2021)

Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. - Alexander Marx 001; Lincen Yang; Matthijs van Leeuwen (SDM, 2021)

A weaker faithfulness assumption based on triple interactions. - Alexander Marx 001; Arthur Gretton; Joris M. Mooij (UAI, 2021)

Formally Justifying MDL-based Inference of Cause and Effect. - Alexander Marx 001; Jilles Vreeken (CoRR, 2021)

Estimating Mutual Information via Geodesic kNN. - Alexander Marx 001; Jonas Fischer (CoRR, 2021)

Information-Theoretic Causal Discovery. - Alexander Marx 001 (2021)

Inferring Cause and Effect in the Presence of Heteroscedastic Noise. - Sascha Xu; Osman Mian; Alexander Marx 001; Jilles Vreeken (ICML, 2022)

Estimating Mutual Information via Geodesic kNN. - Alexander Marx 001; Jonas Fischer (SDM, 2022)

Identifiability Results for Multimodal Contrastive Learning. - Imant Daunhawer; Alice Bizeul; Emanuele Palumbo; Alexander Marx 001; Julia E. Vogt (ICLR, 2023)

On the Identifiability and Estimation of Causal Location-Scale Noise Models. - Alexander Immer; Christoph Schultheiss; Julia E. Vogt; Bernhard Schölkopf; Peter Bühlmann; Alexander Marx 001 (ICML, 2023)

Beyond Normal: On the Evaluation of Mutual Information Estimators. - Pawel Czyz; Frederic Grabowski; Julia E. Vogt; Niko Beerenwinkel; Alexander Marx 001 (NeurIPS, 2023)

Effective Bayesian Heteroscedastic Regression with Deep Neural Networks. - Alexander Immer; Emanuele Palumbo; Alexander Marx 001; Julia E. Vogt (NeurIPS, 2023)

The Mixtures and the Neural Critics: On the Pointwise Mutual Information Profiles of Fine Distributions. - Pawel Czyz; Frederic Grabowski; Julia E. Vogt; Niko Beerenwinkel; Alexander Marx 001 (CoRR, 2023)

Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning. - Mirco Mutti; Riccardo De Santi; Marcello Restelli; Alexander Marx 001; Giorgia Ramponi (ICLR, 2024)

Transfer Learning for Pediatric Glucose Forecasting. - Alain Ryser; Chuhao Feng; Tobias Scheithauer; Marc Pfister; Marie-Anne Burckhardt; Sara Bachmann; Alexander Marx 001; Julia E. Vogt (ML4H@NeurIPS, 2024)

Anomaly Detection by Context Contrasting. - Alain Ryser; Thomas M. Sutter; Alexander Marx 001; Julia E. Vogt (CoRR, 2024)

On the Properties and Estimation of Pointwise Mutual Information Profiles. - Pawel Czyz; Frederic Grabowski; Julia E. Vogt; Niko Beerenwinkel; Alexander Marx 001 (Trans. Mach. Learn. Res., 2025)

A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany. - Ina Dormuth; Sven Franke; Marlies Hafer; Tim Katzke; Alexander Marx 001; Emmanuel Müller; Daniel Neider; Markus Pauly; Jérôme Rutinowski (CoRR, 2025)

Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding. - Marlies Hafer; Alexander Marx 001 (CoRR, 2025)