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137353.2 |
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|a dc
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|a Monteiller, Pierre
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
|e contributor
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|a MIT-IBM Watson AI Lab
|e contributor
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|a Claici, Sebastian
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|a Chien, Edward
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|a Mirzazadeh, Farzaneh
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|a Solomon, Justin
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|a Yurochkin, Mikhail
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|a Alleviating label switching with optimal transport
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|c 2022-01-03T16:40:50Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/137353.2
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|a © 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.
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|a Army Research Office (Grant W911NF1710068)
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|a Air Force Office of Scientific Research (Award FA9550-19-1-031)
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|a National Science Foundation (Grant IIS-1838071)
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|a en
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7 |
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|a Article
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|t Advances in Neural Information Processing Systems
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