Improved Probabilistic Regression Using Diffusion Models
MCML Authors
Gitta Kutyniok
Prof. Dr.
Principal Investigator
Abstract
Gitta Kutyniok
Prof. Dr.
Principal Investigator
Abstract
Probabilistic regression models the entire predictive distribution of a response variable, offering richer insights than classical point estimates and directly allowing for uncertainty quantification. While diffusion-based generative models have shown remarkable success in generating high-dimensional data, their usage in regression tasks often lacks uncertainty-related evaluation. We propose a novel diffusion-based framework for probabilistic regression where we model the full distribution of the diffusion noise, enabling adaptation to diverse tasks and enhanced uncertainty quantification.
inproceedings KBS+26
NLDL 2026
Northern Lights Deep Learning Conference. Tromsø, Norway, Jan 06-08, 2026. To be published. Preprint available.Authors
C. Kneissl • C. Bülte • P. Scholl • G. KutyniokLinks
URLResearch Area
BibTeXKey: KBS+26