Matches in SemOpenAlex for { <https://semopenalex.org/work/W2990020588> ?p ?o ?g. }
- W2990020588 abstract "Bayesian inference problems require sampling or approximating high-dimensional probability distributions. The focus of this paper is on the recently introduced Stein variational gradient descent methodology, a class of algorithms that rely on iterated steepest descent steps with respect to a reproducing kernel Hilbert space norm. This construction leads to interacting particle systems, the mean-field limit of which is a gradient flow on the space of probability distributions equipped with a certain geometrical structure. We leverage this viewpoint to shed some light on the convergence properties of the algorithm, in particular addressing the problem of choosing a suitable positive definite kernel function. Our analysis leads us to considering certain nondifferentiable kernels with adjusted tails. We demonstrate significant performance gains of these in various numerical experiments." @default.
- W2990020588 created "2019-12-05" @default.
- W2990020588 creator A5024045252 @default.
- W2990020588 creator A5065932832 @default.
- W2990020588 creator A5090813467 @default.
- W2990020588 date "2019-12-02" @default.
- W2990020588 modified "2023-09-23" @default.
- W2990020588 title "On the geometry of Stein variational gradient descent" @default.
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- W2990020588 doi "https://doi.org/10.48550/arxiv.1912.00894" @default.
- W2990020588 hasPublicationYear "2019" @default.
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