PREPRINT

Proper losses for discrete generative models

Rafael Frongillo, Dhamma Kimpara, Bo Waggoner

Submitted on 7 November 2022

Abstract

We initiate the study of proper losses for evaluating generative models in the discrete setting. Unlike traditional proper losses, we treat both the generative model and the target distribution as black-boxes, only assuming ability to draw i.i.d. samples. We define a loss to be black-box proper if the generative distribution that minimizes expected loss is equal to the target distribution. Using techniques from statistical estimation theory, we give a general construction and characterization of black-box proper losses: they must take a polynomial form, and the number of draws from the model and target distribution must exceed the degree of the polynomial. The characterization rules out a loss whose expectation is the cross-entropy between the target distribution and the model. By extending the construction to arbitrary sampling schemes such as Poisson sampling, however, we show that one can construct such a loss.

Preprint

Subjects: Computer Science - Machine Learning; Statistics - Machine Learning

URL: http://arxiv.org/abs/2211.03761