Sinkhorn Permutation Variational Marginal Inference

Gonzalo Mena, Erdem Varol, Amin Nejatbakhsh, Eviatar Yemini, Liam Paninski

    Research output: Contribution to journalConference articlepeer-review


    We address the problem of marginal inference for an exponential family defined over the set of permutation matrices. This problem is known to quickly become intractable as the size of the permutation increases, since its involves the computation of the permanent of a matrix, a #P-hard problem. We introduce Sinkhorn variational marginal inference as a scalable alternative, a method whose validity is ultimately justified by the so-called Sinkhorn approximation of the permanent. We demonstrate the effectiveness of our method in the problem of probabilistic identification of neurons in the worm C.elegans.

    Original languageEnglish (US)
    JournalProceedings of Machine Learning Research
    StatePublished - 2019
    Event2nd Symposium on Advances in Approximate Bayesian Inference, AABI 2019 - Vancouver, Canada
    Duration: Dec 8 2019 → …

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software
    • Control and Systems Engineering
    • Statistics and Probability


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