Markus Mueller

I am a Ph.D. candidate at the Econometric Institute at Erasmus University Rotterdam, supervised by Dennis Fok and Kathrin Gruber. My research focuses on deep generative models for tabular data, with a particular emphasis on diffusion models and flow matching. I aim to develop methods that generate high-quality data efficiently, making them practically relevant for real-world applications. More broadly, I am interested in probabilistic machine learning (including uncertainty quantification and variational inference) as well as deep learning for structured data.

Before starting my Ph.D., I completed the Research Master in Business Data Science at the Tinbergen Institute / BDS in Amsterdam. My academic background is rooted in Econometrics and Economics, with a strong focus on causal inference. This foundation enables me to approach modeling problems from both theoretical and applied perspectives.

Selected Publications

ICLR

Continuous Diffusion for Mixed-Type Tabular Data

Markus Mueller, Kathrin Gruber, and Dennis Fok

In International Conference on Learning Representations, 2025

Idea arXiv Bib Code

@inproceedings{mueller2025,
  title = {Continuous {{Diffusion}} for {{Mixed-Type Tabular Data}}},
  booktitle = {International {{Conference}} on {{Learning Representations}}},
  author = {Mueller, Markus and Gruber, Kathrin and Fok, Dennis},
  year = {2025},
  idea = {The main idea was to develop a diffusion model that integrates continuous and categorical effectively and efficiently. We aimed to unify both feature types in continuous space and to balance their losses to avoid implicit importance weights that impact sample quality.}
}

NeurIPS

Continuous Diffusion for Mixed-Type Tabular Data

Markus Mueller, Kathrin Gruber, and Dennis Fok

In Workshop on Synthetic Data Generation with Generative AI, NeurIPS, 2023