Christian Andersson Naesseth: Variational and Monte Carlo methods - Bridging the Gap

Time: Mon 2019-02-25 15.15 - 16.15

Lecturer: Christian Andersson Naesseth

Location: Room F11, Lindstedtsv. 22, KTH.

Abstract: Many recent advances in large scale probabilistic inference rely on the combination of variational and Monte Carlo (MC) methods. The success of these approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. My aim is to show how MC methods can be used not only for stochastic optimization of the variational parameters, but also for defining a more flexible parametric approximation in the first place. First, I will review variational inference (VI). Second, I describe some of the pivotal tools for VI, based on MC methods and stochastic optimization, that have been developed in the last few years. Finally, I will show how we can synthesize sequential Monte Carlo methods and VI to learn more accurate posterior approximations with theoretical guarantees.

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Last changed: Feb 19, 2019