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X-WR-CALNAME:MCMC\, variational inference\, and reverse diffusion Monte Car
lo
X-WR-TIMEZONE:America/Los_Angeles
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BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
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DTSTART:20070311T020000
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DTSTART:20071104T020000
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BEGIN:VEVENT
UID:2023-11-13-yian-ma@cml.ics.uci.edu
DTSTAMP:20231113T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20231113T130000
DTEND;TZID=America/Los_Angeles:20231113T140000
SUMMARY:[CML Seminar] Yian Ma: MCMC\, variational inference\, and reverse d
iffusion Monte Carlo
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Yian Ma\, Assistant Professor\, Halıcıoğlu Data Science Inst
itute\, University of California\, San Diego\n\nTitle: MCMC\, variational
inference\, and reverse diffusion Monte Carlo\n\nAbstract: I will introduc
e some recent progress towards understanding the scalability of Markov cha
in Monte Carlo (MCMC) methods and their comparative advantage with respect
to variational inference. I will fact-check the folklore that variational
inference is fast but biased\, MCMC is unbiased but slow. I will then dis
cuss a combination of the two via reverse diffusion\, which holds promise
of solving some of the multi-modal problems. This talk will be motivated b
y the need for Bayesian computation in reinforcement learning problems as
well as the differential privacy requirements that we face.\n\nhttps://cml
.ics.uci.edu/seminars/2023-11-13-yian-ma
X-ALT-DESC;FMTTYPE=text/html:Yian Ma\, Assistant Profess
or\, Halıcıoğlu Data Science Institute\, University of California\, San
Diego
Title: MCMC\, variational inference\, and reverse dif
fusion Monte Carlo
Abstract: I will introduce some recent pr
ogress towards understanding the scalability of Markov chain Monte Carlo (
MCMC) methods and their comparative advantage with respect to variational
inference. I will fact-check the folklore that variational inference is fa
st but biased\, MCMC is unbiased but slow. I will then discuss a combinati
on of the two via reverse diffusion\, which holds promise of solving some
of the multi-modal problems. This talk will be motivated by the need for B
ayesian computation in reinforcement learning problems as well as the diff
erential privacy requirements that we face.
https://cml.ics.uci.edu/seminars/20
23-11-13-yian-ma
URL:https://cml.ics.uci.edu/seminars/2023-11-13-yian-ma
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