Minisymposium on Model-Based Optimal Experimental Design SIAM CSE 21: Stochastic Gradient BOED

This is the talk, based on our paper A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments that I delivered as an invited speaker at the Minisymposium on Model-Based Optimal Experimental Design at SIAM CSE 21. In the talk, I cover the basics of experimental design with Expected Information Gain (EIG), and then turn to the question of how to efficiently optimize this quantity over a large continuous design space without resorting to inefficient methods like Bayes Opt.