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Poster

Minimax optimal density estimation using a shallow generative model with a one-dimensional latent variable

Hyeok Kyu Kwon · Minwoo Chae

MR1 & MR2 - Number 77
[ ]
Fri 3 May 8 a.m. PDT — 8:30 a.m. PDT

Abstract:

A deep generative model yields an implicit estimator for the unknown distribution or density function of the observation. This paper investigates some statistical properties of the implicit density estimator pursued by VAE-type methods from a nonparametric density estimation framework. More specifically, we obtain convergence rates of the VAE-type density estimator under the assumption that the underlying true density function belongs to a locally Holder class. Remarkably, a near minimax optimal rate with respect to the Hellinger metric can be achieved by the simplest network architecture, a shallow generative model with a one-dimensional latent variable.

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