Adaptive joint distribution learning

Damir Filipovic; Michael Multerer; Paul Schneider

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arXiv · 2110.04829

Adaptive joint distribution learning

Damir Filipovic·Michael Multerer·Paul Schneider

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Abstract

We develop a new framework for estimating joint probability distributions using tensor product reproducing kernel Hilbert spaces (RKHS). Our framework accommodates a low-dimensional, normalized and positive model of a Radon--Nikodym derivative, which we estimate from sample sizes of up to several millions, alleviating the inherent limitations of RKHS modeling. Well-defined normalized and positive conditional distributions are natural by-products to our approach. Our proposal is fast to compute and accommodates learning problems ranging from prediction to classification. Our theoretical findings are supplemented by favorable numerical results.

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BibTeX RIS

Damir Filipovic, Michael Multerer, Paul Schneider. 2021-10-10. Adaptive joint distribution learning. https://arxiv.org/abs/2110.04829

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