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

Displayed time zone: Tijuana, Baja California change

14:00 - 15:30
Probabilistic ProgrammingPLDI Research Papers at 224AB
Chair(s): Martin Hirzel IBM Research
Scalable Verification of Probabilistic Networks
PLDI Research Papers
Steffen Smolka Cornell University, Praveen Kumar Cornell University, David M. Kahn Carnegie Mellon University, USA, Nate Foster Cornell University, Justin Hsu University of Wisconsin-Madison, USA, Dexter Kozen Cornell University, Alexandra Silva University College London
DOI Pre-print Media Attached
Cost Analysis of Nondeterministic Probabilistic Programs
PLDI Research Papers
Peixin Wang Shanghai Jiao Tong University, Hongfei Fu IST Austria, Amir Kafshdar Goharshady IST Austria, Krishnendu Chatterjee IST Austria, Xudong Qin East China Normal University, China, Wenjun Shi East China Normal University, China
Media Attached
Gen: A General-Purpose Probabilistic Programming System with Programmable Inference
PLDI Research Papers
Marco Cusumano-Towner MIT-CSAIL, Feras Saad Massachusetts Institute of Technology, Alexander K. Lew Massachusetts Institute of Technology, USA, Vikash K. Mansinghka MIT
Media Attached
Incremental Precision-Preserving Symbolic Inference for Probabilistic Programs
PLDI Research Papers
Jieyuan Zhang UNSW, Australia, Jingling Xue UNSW Sydney
Media Attached