Streaming, distributed variational inference for Bayesian nonparametrics
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How_Streaming, distributed.pdf
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860.24 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Campbell, Trevor David
Straub, Julian
Fisher, John W
How, Jonathan P
Date Issued
December 2015
Journal
Advances in Neural Information Processing Systems (NIPS 2015)
Publisher
Neural Information Processing Systems Foundation
Citation
Campell, Trevor et al. "Streaming, Distributed Variational Inference for Bayesian Nonparametrics" Advances in Neural Information Processing Systems (NIPS 2015).
Version
Final published version
Abstract
This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models. In the proposed framework, processing nodes receive a sequence of data minibatches, compute a variational posterior for each, and make asynchronous streaming updates to a central model. In contrast to previous algorithms, the proposed framework is truly streaming, distributed, asynchronous, learning-rate-free, and truncation-free. The key challenge in developing the framework, arising from fact that BNP models do not impose an inherent ordering on their components, is finding the correspondence between minibatch and central BNP posterior components before performing each update. To address this, the paper develops a combinatorial optimization problem over component correspondences, and provides an efficient solution technique. The paper concludes with an application of the methodology to the DP mixture model, with experimental results demonstrating its practical scalability and performance.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/5876-streaming-distributed-variational-inference-for-bayesian-nonparametrics
