Importance Weighted Generative Networks
Machine Learning
2020-09-08 v3 Machine Learning
Abstract
Deep generative networks can simulate from a complex target distribution, by minimizing a loss with respect to samples from that distribution. However, often we do not have direct access to our target distribution - our data may be subject to sample selection bias, or may be from a different but related distribution. We present methods based on importance weighting that can estimate the loss with respect to a target distribution, even if we cannot access that distribution directly, in a variety of settings. These estimators, which differentially weight the contribution of data to the loss function, offer both theoretical guarantees and impressive empirical performance.
Cite
@article{arxiv.1806.02512,
title = {Importance Weighted Generative Networks},
author = {Maurice Diesendruck and Ethan R. Elenberg and Rajat Sen and Guy W. Cole and Sanjay Shakkottai and Sinead A. Williamson},
journal= {arXiv preprint arXiv:1806.02512},
year = {2020}
}