Gaussian Process Bandit Optimization of theThermodynamic Variational Objective

10/29/2020 ∙ by Vu Nguyen, et al. ∙ 8

Achieving the full promise of the Thermodynamic Variational Objective (TVO),a recently proposed variational lower bound on the log evidence involving a one-dimensional Riemann integral approximation, requires choosing a "schedule" ofsorted discretization points. This paper introduces a bespoke Gaussian processbandit optimization method for automatically choosing these points. Our approach not only automates their one-time selection, but also dynamically adaptstheir positions over the course of optimization, leading to improved model learning and inference. We provide theoretical guarantees that our bandit optimizationconverges to the regret-minimizing choice of integration points. Empirical validation of our algorithm is provided in terms of improved learning and inference inVariational Autoencoders and Sigmoid Belief Networks.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 8

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.