Markus Mueller is a final-year PhD candidate at the Econometric Institute. His research focuses on deep generative models for tabular data, with a particular emphasis on diffusion models and flow matching.
Job market paper
Continuous Diffusion for Mixed-Type Tabular Data
Score-based generative models, commonly referred to as diffusion models, have proven to be successful at generating text and image data. However, their adaptation to mixed-type tabular data remains underexplored. In this work, we propose CDTD, a Continuous Diffusion model for mixed-type Tabular Data. CDTD is based on a novel combination of score matching and score interpolation to enforce a unified continuous noise distribution for both continuous and categorical features. We explicitly acknowledge the necessity of homogenizing distinct data types by relying on model-specific loss calibration and initialization schemes. To further address the high heterogeneity in mixed-type tabular data, we introduce adaptive feature or type-specific noise schedules. These ensure balanced generative performance across features and optimize the allocation of model capacity across features and diffusion time. Our experimental results show that CDTD consistently outperforms state-of-the-art benchmark models, captures feature correlations exceptionally well, and that heterogeneity in the noise schedule design boosts sample quality.
CV
Download CV (pdf)
References
- Prof. Dennis Fok
- Prof. Kathrin Gruber
Contact
Email: mueller@ese.eur.nl
Personal website: https://muellermarkus.github.io/