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X-WR-CALNAME:Towards Verified Stochastic Variational Inference for Probabil
 istic Programs - Hongseok Yang\, Professor\, KAIST
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TZID:America/Vancouver
TZUNTIL:20210314T100000Z
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TZNAME:PST
DTSTART:20181104T020000
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TZOFFSETTO:-0800
RDATE:20191103T020000
RDATE:20201101T020000
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DTSTAMP:20260305T225241Z
CLASS:PUBLIC
CREATED:20190722T171844Z
DESCRIPTION:Abstract: Probabilistic programming is the idea of writing mode
 ls from statistics and machine learning using program notations and reason
 ing about these models using generic inference engines. Recently its combi
 nation with deep learning has been explored intensely\, which led to the d
 evelopment of so called deep probabilistic programming languages\, such as
  Pyro\, Edward and ProbTorch. At the core of this development lie inferenc
 e engines based on stochastic variational inference algorithms. When asked
  to find information about the posterior distribution of a model written i
 n such a language…
DTSTART;TZID=America/Vancouver:20190726T120000
DTEND;TZID=America/Vancouver:20190726T130000
LAST-MODIFIED:20210611T171537Z
LOCATION:Hugh Dempster Building\, 6245 Agronomy Road
SUMMARY:Towards Verified Stochastic Variational Inference for Probabilistic
  Programs - Hongseok Yang\, Professor\, KAIST
TRANSP:OPAQUE
URL:https://caida.ubc.ca/event/towards-verified-stochastic-variational-infe
 rence-probabilistic-programs-hongseok-yang
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