Improved probabilistic regression using diffusion models

Carlo Kneissl, Christopher Bülte, Philipp Scholl, Gitta Kutyniok

Keywords: Diffusion models, Uncertainty Quantification, Probabilstic Regression

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.

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Submission Number: 17