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arXiv · 2004.10255

Convex Nonparanormal Regression

Yonatan Woodbridge·Gal Elidan·Ami Wiesel
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Abstract

Quantifying uncertainty in predictions or, more generally, estimating the posterior conditional distribution, is a core challenge in machine learning and statistics. We introduce Convex Nonparanormal Regression (CNR), a conditional nonparanormal approach for coping with this task. CNR involves a convex optimization of a posterior defined via a rich dictionary of pre-defined non linear transformations on Gaussians. It can fit an arbitrary conditional distribution, including multimodal and non-symmetric posteriors. For the special but powerful case of a piecewise linear dictionary, we provide a closed form of the posterior mean which can be used for point-wise predictions. Finally, we demonstrate the advantages of CNR over classical competitors using synthetic and real world data.

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BibTeXRIS

Yonatan Woodbridge, Gal Elidan, Ami Wiesel. 2021-04-04. Convex Nonparanormal Regression. https://doi.org/10.1109/lsp.2021.3102849

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