3 accepted papers
Computing mutual information (MI) of random variables lacks a closed-form in nontrivial models. Variational MI approximations are widely used as flexible estimators for this purpose, but computing them typically requires solving a costly nonconvex optimization. We prove that a widely used class of v…
Mutual information (MI) is a commonly adopted utility function in Bayesian optimal experimental design (BOED). While theoretically appealing, MI evaluation poses a significant computational burden for most real world applications. As a result, many algorithms utilize MI bounds as proxies that lack r…
In many sequential planning applications a natural approach to generating high quality plans is to maximize an information reward such as mutual information (MI). Unfortunately, MI lacks a closed form in all but trivial models, and so must be estimated. In applications where the cost of plan executi…