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SUMMARY:Learning Dynamical Transport without Data
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Michael Albergo (Harvard) \nTitle: Learning Dynamical Transport without Data \nAbstract: Algorithms based on dynamical transport of measure\, such as score-based diffusion models\, have resulted in great progress in the field of generative modeling. However\, these algorithms rely on access to an abundance of data from the target distribution. A complementary problem to this is learning to generate samples from a target distribution when only given query access to the unnormalized log-likelihood or energy function associated to it\, with myriad application in statistical physics\, chemistry\, and Bayesian inference. I will present an algorithm based on dynamical transport to sample from a target distribution in this context\, which can be seen as an augmentation of annealed importance sampling and sequential Monte Carlo. Time permitting\, I will also discuss how to generalize these ideas to dynamics of discrete distributions. This is joint work with Eric Vanden-Eijnden\, Peter Holderrieth\, and Tommi Jaakkola. \n 
URL:https://cmsa.fas.harvard.edu/event/newtech_4225/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
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