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Gibbs sampling is the de facto Markov chain Monte Carlo method used for inference and learning on large scale graphical models. For complicated factor graphs with lots of factors, the performance of Gibbs sampling can be limited by the computational cost of executing a single update step of the Markov chain. This cost is proportional to the degree of the graph, the number of factors adjacent to each variable. In this paper, we show how this cost can be reduced by using minibatching: subsampling the factors to form an estimate of their sum. We introduce several minibatched variants of Gibbs, show that they can be made unbiased, prove bounds on their convergence rates, and show that under some conditions they can result in asymptotic single-update-run-time speedups over plain Gibbs sampling.
Author Information
Christopher De Sa (Cornell)
Zhiting Chen (Cornell University)
Masters student at Cornell University -Data Science -Machine learning -Electrical Engineering
Wong (Stanford university)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: Minibatch Gibbs Sampling on Large Graphical Models »
Fri Jul 13th 02:20 -- 02:40 PM Room A4
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