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Oral
Online Variational Filtering and Parameter Learning
Andrew Campbell · Yuyang Shi · Thomas Rainforth · Arnaud Doucet

Tue Dec 07 12:20 AM -- 12:35 AM (PST) @
in Oral Session 1: Generative Modeling »

We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data. As per standard batch variational techniques, we use stochastic gradients to simultaneously optimize a lower bound on the log evidence with respect to both model parameters and a variational approximation of the states' posterior distribution. However, unlike existing approaches, our method is able to operate in an entirely online manner, such that historic observations do not require revisitation after being incorporated and the cost of updates at each time step remains constant, despite the growing dimensionality of the joint posterior distribution of the states. This is achieved by utilizing backward decompositions of this joint posterior distribution and of its variational approximation, combined with Bellman-type recursions for the evidence lower bound and its gradients. We demonstrate the performance of this methodology across several examples, including high-dimensional SSMs and sequential Variational Auto-Encoders.

Keywords: Generative Model · Online Learning

Author Information

Andrew Campbell (University of Oxford)
Yuyang Shi (University of Oxford)
Thomas Rainforth (University of Oxford)
Arnaud Doucet (Google DeepMind)

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