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Reparameterizing the Birkhoff polytope for variational permutation inference

Abstract:

Many matching, tracking, sorting, and ranking problems require probabilistic reasoning about possible permutations, a set that grows factorially with dimension. Combinatorial optimization algorithms may enable efficient point estimation, but fully Bayesian inference poses a severe challenge in this high-dimensional, discrete space. To surmount this challenge, we start by relaxing the discrete set of permutation matrices to its convex hull the Birkhoff polytope, the set of doubly-stochastic ma...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publication website:
http://proceedings.mlr.press/v84/linderman18a.html

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
ORCID:
0000-0003-4432-9679
Publisher:
Proceedings of Machine Learning Research
Journal:
Proceedings of Machine Learning Research More from this journal
Volume:
84
Publication date:
2018-04-01
Acceptance date:
2017-11-17
Event title:
International Conference on Artificial Intelligence and Statistics 2018
Event location:
Canary Islands
Event start date:
2018-04-09
Event end date:
2018-04-11
Language:
English
Keywords:
Pubs id:
1136148
Local pid:
pubs:1136148
Deposit date:
2020-10-05

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