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Activity Number: 8
Type: Invited
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #314332
Title: Firefly Monte Carlo: Exact MCMC with Subsets of Data
Author(s): Ryan P. Adams* and Dougal Maclaurin
Companies: Harvard University and Harvard University
Keywords: MCMC ; scalability ; Bayesian
Abstract:

One of the challenges of building statistical models for large data sets is balancing the correctness of inference procedures against computational realities. In the context of Bayesian procedures, the pain of such computations has been particularly acute as it has appeared that algorithms such as Markov chain Monte Carlo necessarily need to touch all of the data at each iteration in order to arrive at a correct answer. Several recent proposals have been made to use subsets of data to perform MCMC in ways analogous to stochastic gradient descent. Unfortunately, these proposals have only provided approximations. Firefly Monte Carlo is an auxiliary variable method that uses randomized subsets of data to achieve valid transition operators, with connections to recent developments in pseudo-marginal MCMC. It leaves the true full-data posterior invariant, while achieving significant improvements in wallclock performance.


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