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).

Conference Day
Mon 24 Jun

Displayed time zone: Tijuana, Baja California change

14:00 - 15:30
Probabilistic ProgrammingPLDI Research Papers at 224AB
Chair(s): Martin HirzelIBM Research
Scalable Verification of Probabilistic Networks
PLDI Research Papers
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
Cost Analysis of Nondeterministic Probabilistic Programs
PLDI Research Papers
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
Media Attached
Gen: A General-Purpose Probabilistic Programming System with Programmable Inference
PLDI Research Papers
Marco Cusumano-TownerMIT-CSAIL, Feras A. SaadMassachusetts Institute of Technology, Alexander K. LewMassachusetts Institute of Technology, USA, Vikash K. MansinghkaMIT
Media Attached
Incremental Precision-Preserving Symbolic Inference for Probabilistic Programs
PLDI Research Papers
Jieyuan ZhangUNSW, Australia, Jingling XueUNSW Sydney
Media Attached