Variational information planning for sequential decision making

Jason Pacheco, John W. Fisher

Research output: Contribution to conferencePaperpeer-review


We consider the setting of sequential decision making where, at each stage, potential actions are evaluated based on expected reduction in posterior uncertainty, given by mutual information (MI). As MI typically lacks a closed form, we propose an approach which maintains variational approximations of, both, the posterior and MI utility. Our planning objective extends an established variational bound on MI to the setting of sequential planning. The result, variational information planning (VIP), is an efficient method for sequential decision making. We further establish convexity of the variational planning objective and, under conditional exponential family approximations, we show that the optimal MI bound arises from a relaxation of the well-known exponential family moment matching property. We demonstrate VIP for sensor selection, experiment design, and active learning, where it meets or exceeds methods requiring more computation, or those specialized to the task.

Original languageEnglish (US)
StatePublished - 2020
Externally publishedYes
Event22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019 - Naha, Japan
Duration: Apr 16 2019Apr 18 2019


Conference22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019

ASJC Scopus subject areas

  • Artificial Intelligence
  • Statistics and Probability


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