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Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Vu Nguyen · Vaden Masrani · Rob Brekelmans · Michael A Osborne · Frank Wood

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1364

Achieving the full promise of the Thermodynamic Variational Objective (TVO), a recently proposed variational inference objective that lower-bounds the log evidence via one-dimensional Riemann integration, requires choosing a ``schedule'' of sorted discretization points. This paper introduces a bespoke Gaussian process bandit optimization method for automatically choosing these points. Our approach not only automates their one-time selection, but also dynamically adapts their positions over the course of optimization, leading to improved model learning and inference. We provide theoretical guarantees that our bandit optimization converges to the regret-minimizing choice of integration points. Empirical validation of our algorithm is provided in terms of improved learning and inference in Variational Autoencoders and sigmoid belief networks.

Author Information

Vu Nguyen (University of Oxford)
Vaden Masrani (University of British Columbia)
Rob Brekelmans (University of Southern California)
Michael A Osborne (U Oxford)
Frank Wood (University of British Columbia)

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