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Activity Number: 10 - General-Purpose Fast Accurate Bayesian Computation at Big-Data Scale
Type: Invited
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #322400 View Presentation
Title: Coresets for Scalable Bayesian Logistic Regression
Author(s): Tamara Broderick*
Companies: MIT
Keywords:
Abstract:

The use of Bayesian models in large-scale data settings is attractive because of the rich hierarchical relationships, uncertainty quantification, and prior specification they provide. Standard Bayesian inference algorithms are computationally expensive, however, making their direct application to large datasets difficult or infeasible. Recent work on scaling Bayesian inference has focused on modifying the underlying algorithms to, for example, use only a random data subsample at each iteration. We leverage the insight that data is often redundant to instead obtain a weighted subset of the data (called a coreset) that is much smaller than the original dataset. We can then use this small coreset in any number of existing posterior inference algorithms without modification. In this paper, we develop an efficient coreset construction algorithm for Bayesian logistic regression models. We provide theoretical guarantees on the size and approximation quality of the coreset -- both for fixed, known datasets, and in expectation for a wide class of data generative models. The proposed approach also permits efficient construction of the coreset in both streaming and parallel


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