Alleviating label switching with optimal transport
Name
NeurIPS-2019-alleviating-label-switching-with-optimal-transport-Paper.pdf
Description
Published version
Size
3.68 MB
Format
Unknown
Checksum (MD5)
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Author(s)
Monteiller, Pierre
Claici, Sebastian
Chien, Edward
Mirzazadeh, Farzaneh
Solomon, Justin
Yurochkin, Mikhail
Date Issued
December 2019
Journal
Advances in Neural Information Processing Systems
Citation
2019. "Alleviating label switching with optimal transport." Advances in Neural Information Processing Systems, 32.
Version
Final published version
Abstract
© 2019 Neural information processing systems foundation. All rights reserved. Label switching is a phenomenon arising in mixture model posterior inference that prevents one from meaningfully assessing posterior statistics using standard Monte Carlo procedures. This issue arises due to invariance of the posterior under actions of a group; for example, permuting the ordering of mixture components has no effect on the likelihood. We propose a resolution to label switching that leverages machinery from optimal transport. Our algorithm efficiently computes posterior statistics in the quotient space of the symmetry group. We give conditions under which there is a meaningful solution to label switching and demonstrate advantages over alternative approaches on simulated and real data.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
MIT-IBM Watson AI Lab
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/2019/hash/c2ae5cb2426d96ed19a50b0b7d7c8e11-Abstract.html
