Mon 24 Jun 2019 15:00 - 15:20 at 224AB - Probabilistic Programming Chair(s): Martin Hirzel

We present \textsc{ISymb} an incremental
symbolic inference framework
for probabilistic programs in situations when
some loop-manipulated array data, upon
which their probabilistic models are conditioned,
undergoes small changes. To tackle the path
explosion challenge, \textsc{ISymb} is intra-procedurally
path-sensitive except
that it conducts a ``meet-over-all-paths''
analysis within an iteration of a loop
(conditioned on some observed array data).
By recomputing only the
probability distributions for the
paths affected, \textsc{ISymb} avoids
expensive symbolic inference from scratch while
also being
precision-preserving. Our evaluation with a set of
existing benchmarks shows that
\textsc{ISymb} can lead to orders of magnitude
performance improvements compared to its
non-incremental counterpart (under small changes in
observed array data).

Mon 24 Jun
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14:00 - 15:30: PLDI Research Papers - Probabilistic Programming at 224AB
Chair(s): Martin HirzelIBM Research
pldi-2019-papers14:00 - 14:20
Steffen SmolkaCornell University, Praveen KumarCornell University, David M. KahnCarnegie Mellon University, USA, Nate FosterCornell University, Justin HsuUniversity of Wisconsin-Madison, USA, Dexter KozenCornell University, Alexandra SilvaUniversity College London
DOI Pre-print Media Attached
pldi-2019-papers14:20 - 14:40
Peixin WangShanghai Jiao Tong University, Hongfei FuIST Austria, Amir Kafshdar GoharshadyIST Austria, Krishnendu ChatterjeeIST Austria, Xudong QinEast China Normal University, China, Wenjun ShiEast China Normal University, China
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pldi-2019-papers14:40 - 15:00
Marco Cusumano-TownerMIT-CSAIL, Feras SaadMassachusetts Institute of Technology, Alexander K. LewMassachusetts Institute of Technology, USA, Vikash MansinghkaMIT
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pldi-2019-papers15:00 - 15:20
Jieyuan ZhangUNSW, Australia, Jingling XueUNSW Sydney
Media Attached