In machine learning and computer vision, optimal transport has had significant success in learning generative models and defining metric distances between structured and stochastic data objects, that can be cast as probability measures.
The key element of optimal transport is the so called lifting of an exact cost (distance) function, defined on the sample space, to a cost (distance) between probability measures over the sample space. However, in many real life applications the cost is stochastic: e.g., the unpredictable traffic flow affects the cost of transportation between a factory and an outlet. To take this stochasticity into account, we introduce a Bayesian framework for inferring the optimal transport plan distribution induced by the stochastic cost, allowing for a principled way to include prior information and to model the induced stochasticity on the transport plans. Additionally, we tailor an HMC method to sample from the resulting transport plan posterior distribution.
Original languageEnglish
Title of host publicationProceedings of Asian Conference on Machine Learning
Number of pages16
Publication statusPublished - 2021
MoE publication typeA4 Conference publication
EventAsian Conference on Machine Learning - Virtual, Online
Duration: 17 Nov 202119 Nov 2021
Conference number: 13

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)2640-3498


ConferenceAsian Conference on Machine Learning
Abbreviated titleACML
CityVirtual, Online


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