Statistics > Machine Learning
[Submitted on 31 Oct 2015 (v1), last revised 12 Aug 2016 (this version, v3)]
Title:Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions
View PDFAbstract:Several recent works have explored stochastic gradient methods for variational inference that exploit the geometry of the variational-parameter space. However, the theoretical properties of these methods are not well-understood and these methods typically only apply to conditionally-conjugate models. We present a new stochastic method for variational inference which exploits the geometry of the variational-parameter space and also yields simple closed-form updates even for non-conjugate models. We also give a convergence-rate analysis of our method and many other previous methods which exploit the geometry of the space. Our analysis generalizes existing convergence results for stochastic mirror-descent on non-convex objectives by using a more general class of divergence functions. Beyond giving a theoretical justification for a variety of recent methods, our experiments show that new algorithms derived in this framework lead to state of the art results on a variety of problems. Further, due to its generality, we expect that our theoretical analysis could also apply to other applications.
Submission history
From: Mohammad Emtiyaz Khan [view email][v1] Sat, 31 Oct 2015 15:56:32 UTC (729 KB)
[v2] Sun, 12 Jun 2016 23:47:06 UTC (1,652 KB)
[v3] Fri, 12 Aug 2016 00:47:22 UTC (1,650 KB)
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