BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Bayesian Inference for Optimal Transport with Stochastic Cost - An
 ton Mallasto
DTSTART:20210330T140000Z
DTEND:20210330T150000Z
UID:TALK158512@talks.cam.ac.uk
CONTACT:96082
DESCRIPTION:In machine learning and computer vision\, optimal transport (O
 T) has had significant success in learning generative models and defining 
 metrics between structured and stochastic data objects\, that can be cast 
 as probability measures. The key element of optimal transport is the so ca
 lled lifting of an \\emph{exact} cost (distance) function\, defined on the
  sample space\, to a cost (distance) between probability measures over the
  sample space. This is carried out by minimizing the total transportation 
 cost between two measures\, resulting in the OT plan.\n\nHowever\, in many
  real life applications the cost is stochastic: for example\, an unpredict
 able traffic flow affects the cost of transportation between a factory and
  an outlet. In this talk\, we devise a Bayesian approach for inferring a d
 istribution over the OT plans in such random settings.
LOCATION:https://us02web.zoom.us/j/88368107456?pwd=dTkvaXBaaCszMnB4ck1CRXN
 XVWtTQT09
END:VEVENT
END:VCALENDAR