Skip to main content
SHARE
Publication

A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models

by Jiaxin Zhang, Sirui Bi, Guannan Zhang
Publication Type
Conference Paper
Book Title
Proceedings of Machine Learning Research
Publication Date
Page Numbers
3745 to 3753
Volume
130
Issue
1
Conference Name
International Conference on Artificial Intelligence and Statistics
Conference Location
Virtual, Tennessee, United States of America
Conference Sponsor
International Conference on Artificial Intelligence and Statistics
Conference Date
-

Bayesian experimental design (BED) is to answer the question that how to choose designs that maximize the information gathering. For implicit models, where the likelihood is intractable but sampling is possible, conventional BED methods have difficulties in efficiently estimating the posterior distribution and maximizing the mutual information (MI) between data and parameters. Recent work proposed the use of gradient ascent to maximize a lower bound on MI to deal with these issues. However, the approach requires a sampling path to compute the pathwise gradient of the MI lower bound with respect to the design variables, and such a pathwise gradient is usually inaccessible for implicit models. In this paper, we propose a novel approach that leverages recent advances in stochastic approximate gradient ascent incorporated with a smoothed variational MI estimator for efficient and robust BED. Without the necessity of pathwise gradients, our approach allows the design process to be achieved through a unified procedure with an approximate gradient for implicit models. Several experiments show that our approach outperforms baseline methods, and significantly improves the scalability of BED in high-dimensional problems.