A Stein variational Newton method

Gianluca Detommaso, Tiangang Cui, Youssef Marzouk, Alessio Spantini, Robert Scheichl

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems (NIPS) 2018
Publication statusAcceptance date - 8 Jun 2018


  • stat.ML
  • cs.LG
  • cs.NA

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