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X-WR-CALNAME:Variational Methods for Bayesian Optimal Experimental Design
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UID:2023-02-13-noble-kennamer@cml.ics.uci.edu
DTSTAMP:20230213T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20230213T130000
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SUMMARY:[CML Seminar] Noble Kennamer: Variational Methods for Bayesian Opti
mal Experimental Design
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Noble Kennamer\, PhD Student\, Department of Computer Science\,
University of California\, Irvine\n\nTitle: Variational Methods for Bayes
ian Optimal Experimental Design\n\nAbstract: Bayesian optimal experimental
design is a sub-field of statistics focused on developing methods to make
efficient use of experimental resources. Any potential design is evaluate
d in terms of a utility function\, such as the expected information gain (
EIG)\; unfortunately\, under most circumstances the EIG is intractable to
evaluate. In this talk we build off successful variational approaches\, wh
ich optimize a parameterized variational model with respect to bounds on t
he EIG. We present a novel neural architecture that allows experimenters t
o optimize a single variational model that can estimate the EIG for potent
ially infinitely many designs. We demonstrate the effectiveness of our tec
hnique on generalized linear models\, showing that our method greatly impr
oves accuracy over existing approximation strategies with far better sampl
e efficiency.\n\nhttps://cml.ics.uci.edu/seminars/2023-02-13-noble-kenname
r
X-ALT-DESC;FMTTYPE=text/html:Noble Kennamer\, PhD Studen
t\, Department of Computer Science\, University of California\, Irvine
Title: Variational Methods for Bayesian Optimal Experimental De
sign
Abstract: Bayesian optimal experimental design is a sub
-field of statistics focused on developing methods to make efficient use o
f experimental resources. Any potential design is evaluated in terms of a
utility function\, such as the expected information gain (EIG)\; unfortuna
tely\, under most circumstances the EIG is intractable to evaluate. In thi
s talk we build off successful variational approaches\, which optimize a p
arameterized variational model with respect to bounds on the EIG. We prese
nt a novel neural architecture that allows experimenters to optimize a sin
gle variational model that can estimate the EIG for potentially infinitely
many designs. We demonstrate the effectiveness of our technique on genera
lized linear models\, showing that our method greatly improves accuracy ov
er existing approximation strategies with far better sample efficiency.
https://cml.ics.uci.edu/seminars/2023-02-13-noble-kennamer
URL:https://cml.ics.uci.edu/seminars/2023-02-13-noble-kennamer
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