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• W2806674516 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." @default.
• W2806674516 created "2018-06-13" @default.
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• W2806674516 date "2018-06-08" @default.
• W2806674516 modified "2023-09-27" @default.
• W2806674516 title "A Stein variational Newton method" @default.
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• W2806674516 hasPublicationYear "2018" @default.
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