A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
This work addresses the computational bottleneck in experimental design for researchers and practitioners, offering a more scalable method, though it is incremental as it builds on existing variational bounds.
The paper tackles the problem of Bayesian optimal experimental design by introducing a fully stochastic gradient approach that unifies variational and design optimization, enabling effective design in higher-dimensional settings than previous methods.
We introduce a fully stochastic gradient based approach to Bayesian optimal experimental design (BOED). Our approach utilizes variational lower bounds on the expected information gain (EIG) of an experiment that can be simultaneously optimized with respect to both the variational and design parameters. This allows the design process to be carried out through a single unified stochastic gradient ascent procedure, in contrast to existing approaches that typically construct a pointwise EIG estimator, before passing this estimator to a separate optimizer. We provide a number of different variational objectives including the novel adaptive contrastive estimation (ACE) bound. Finally, we show that our gradient-based approaches are able to provide effective design optimization in substantially higher dimensional settings than existing approaches.