EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

TL;DR

EDDI introduces a Bayesian experimental design framework utilizing a novel Partial VAE to efficiently identify high-value information, reducing costs and improving decision quality in healthcare and machine learning tasks.

Contribution

The paper presents a new Partial VAE model and an acquisition strategy for efficient information discovery, balancing cost and decision accuracy.

Findings

Cost reduction at the same decision quality

Improved decision quality at the same cost

Validated on multiple benchmarks and healthcare applications

Abstract

Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant information enables better decision making, but may be costly. How can we trade off the desire to make good decisions by acquiring further information with the cost of performing that acquisition? To this end, we propose a principled framework, named EDDI (Efficient Dynamic Discovery of high-value Information), based on the theory of Bayesian experimental design. In EDDI, we propose a novel partial variational autoencoder (Partial VAE) to predict missing data entries problematically given any subset of the observed ones, and combine it with an acquisition function that maximizes expected information gain on a set of target variables. We show cost reduction at the same decision quality and improved decision quality at the same cost in multiple machine learning benchmarks and two real-world health-care applications.