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arXiv · 2409.04231

Generative Modelling via Quantile Regression

Johannes Schmidt-Hieber·Petr Zamolodtchikov
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Abstract

We link conditional generative modelling to quantile regression. We propose a suitable loss function and derive minimax convergence rates for the associated risk under smoothness assumptions imposed on the conditional distribution. To establish the lower bound, we show that nonparametric regression can be seen as a sub-problem of the considered generative modelling framework. Finally, we discuss extensions of our work to generate data from multivariate distributions.

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Johannes Schmidt-Hieber, Petr Zamolodtchikov. 2024-09-06. Generative Modelling via Quantile Regression. https://arxiv.org/abs/2409.04231

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