Poster

Kernel-Based Tests for Likelihood-Free Hypothesis Testing

Patrik Robert Gerber · Tianze Jiang · Yury Polyanskiy · Rui Sun

Great Hall & Hall B1+B2 (level 1) #1008
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Thu 14 Dec 8:45 a.m. PST — 10:45 a.m. PST

Abstract: Given $n$ observations from two balanced classes, consider the task of labeling an additional $m$ inputs that are known to all belong to \emph{one} of the two classes. Special cases of this problem are well-known: with completeknowledge of class distributions ($n=\infty$) theproblem is solved optimally by the likelihood-ratio test; when$m=1$ it corresponds to binary classification; and when $m\approx n$ it is equivalent to two-sample testing. The intermediate settings occur in the field of likelihood-free inference, where labeled samples are obtained by running forward simulations and the unlabeled sample is collected experimentally. In recent work it was discovered that there is a fundamental trade-offbetween $m$ and $n$: increasing the data sample $m$ reduces the amount $n$ of training/simulationdata needed. In this work we (a) introduce a generalization where unlabeled samples come from a mixture of the two classes -- a case often encountered in practice; (b) study the minimax sample complexity for non-parametric classes of densities under \textit{maximum meandiscrepancy} (MMD) separation; and (c) investigate the empirical performance of kernels parameterized by neural networks on two tasks: detectionof the Higgs boson and detection of planted DDPM generated images amidstCIFAR-10 images. For both problems we confirm the existence of the theoretically predicted asymmetric $m$ vs $n$ trade-off.

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