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Poster
On Variational Bounds of Mutual Information
Ben Poole · Sherjil Ozair · Aäron van den Oord · Alexander Alemi · George Tucker

Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #86
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Estimating and optimizing Mutual Information (MI) is core to many problems in machine learning, but bounding MI in high dimensions is challenging. To establish tractable and scalable objectives, recent work has turned to variational bounds parameterized by neural networks. However, the relationships and tradeoffs between these bounds remains unclear. In this work, we unify these recent developments in a single framework. We find that the existing variational lower bounds degrade when the MI is large, exhibiting either high bias or high variance. To address this problem, we introduce a continuum of lower bounds that encompasses previous bounds and flexibly trades off bias and variance. On high-dimensional, controlled problems, we empirically characterize the bias and variance of the bounds and their gradients and demonstrate the effectiveness of these new bounds for estimation and representation learning.

Keywords: Approximate Inference · Bayesian Deep Learning · Information Theory and Estimation · Optimization

Author Information

Ben Poole (Google Brain)
Sherjil Ozair (University of Montreal)
Aäron van den Oord (Google Deepmind)
Alexander Alemi (Google)
George Tucker (Google Brain)

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