A Stein variational Newton method
Name
8130-a-stein-variational-newton-method.pdf
Description
Published version
Size
459.99 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Marzouk, Youssef M
Spantini, Alessio
Date Issued
December 2018
Journal
32nd Conference on Neural Information Processing Systems (NeurIPS 2018)
Publisher
Curran Associates, Inc.
Citation
Detommaso, Gianluca et al. “A Stein variational Newton method.” Paper presented at the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal, Canada, 3-8 December 2018, Curran Associates, Inc. © 2018 The Author(s)
Version
Final published version
Abstract
Stein variational gradient descent (SVGD) was recently proposed as a general purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]: it minimizes the Kullback-Leibler divergence between the target distribution and its approximation by implementing a form of functional gradient descent on a reproducing kernel Hilbert space. In this paper, we accelerate and generalize the SVGD algorithm by including second-order information, thereby approximating a Newton-like iteration in function space. We also show how second-order information can lead to more effective choices of kernel. We observe significant computational gains over the original SVGD algorithm in multiple test cases.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/8130-a-stein-variational-newton-method
