Adaptive joint distribution learning

• URL: http://arxiv.org/abs/2110.04829v5
• Date: Tue, 24 Sep 2024 10:56:04 GMT
• Title: Adaptive joint distribution learning
• Authors: Damir Filipovic, Michael Multerer, Paul Schneider,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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