Alleviating label switching with optimal transport
© 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...
Main Authors: | Monteiller, Pierre (Author), Claici, Sebastian (Author), Chien, Edward (Author), Mirzazadeh, Farzaneh (Author), Solomon, Justin (Author), Yurochkin, Mikhail (Author) |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), MIT-IBM Watson AI Lab (Contributor) |
Format: | Article |
Language: | English |
Published: |
2022-01-03T16:40:50Z.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
Hierarchical optimal transport for document representation
by: Yurochkin, Mikhail, et al.
Published: (2022) -
Alleviating label switching with optimal transport
Published: (2021) -
Stochastic wasserstein barycenters
by: Solomon, Justin, et al.
Published: (2021) -
Structure as simplification : transportation tools for understanding data
by: Claici, Sebastian.
Published: (2020) -
Parallel Streaming Wasserstein Barycenters
by: Staib, Matthew, et al.
Published: (2021)