Automating experiments with neural information estimators

The idea that good experiments maximize information gain has a long history (e.g. Lindley, 1956). A key obstacle to using this insight to automate experiment design has been the difficulty of estimating information gain. In the last few years new methods have become available for casting information estimation as an optimization problem. Using these ideas, we describe variational bounds on expected information gain. Deep neural networks and gradient-based optimization then allow efficient optimization of these bounds even for complex data models. We apply these ideas to experiment design, adaptive surveys, and computer adaptive testing.