We propose a new algorithm---Stochastic Proximal Langevin Algorithm (SPLA)---for sampling from a log concave distribution. Our method is a generalization of the Langevin algorithm to potentials expressed as the sum of one stochastic smooth term and multiple stochastic nonsmooth terms. In each iteration, our splitting technique only requires access to a stochastic gradient of the smooth term and a stochastic proximal operator for each of the nonsmooth terms. We establish nonasymptotic sublinear and linear convergence rates under convexity and strong convexity of the smooth term, respectively, expressed in terms of the KL divergence and Wasserstein distance. We illustrate the efficiency of our sampling technique through numerical simulations on a Bayesian learning task.
Author Information
Adil SALIM (KAUST)
Dmitry Koralev (KAUST)
Peter Richtarik (KAUST)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Poster: Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates »
Tue Dec 10th 05:30 -- 07:30 PM Room East Exhibition Hall B + C
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