Mon 24 Jun 2019 14:00 - 14:20 at 224AB - Probabilistic Programming Chair(s): Martin Hirzel

This paper presents McNetKAT, a scalable tool for verifying
probabilistic network programs. McNetKAT is based on a new semantics
for the guarded and history-free fragment of Probabilistic NetKAT in
terms of finite-state, absorbing Markov chains. This view allows the
semantics of all programs to be computed exactly, enabling
construction of an automatic verification tool. Domain-specific
optimizations and a parallelizing backend enable
McNetKAT to analyze networks with thousands of nodes,
automatically reasoning about general properties such as probabilistic
program equivalence and refinement, as well as networking properties
such as resilience to failures. We evaluate McNetKAT's scalability
using real-world topologies, compare its performance against
state-of-the-art tools, and develop an extended case study on a
recently proposed data center network design.

Mon 24 Jun
Times are displayed in time zone: Tijuana, Baja California change

14:00 - 15:30: Probabilistic ProgrammingPLDI Research Papers at 224AB
Chair(s): Martin HirzelIBM Research
14:00 - 14:20
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
14:20 - 14:40
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
14:40 - 15:00
Gen: A General-Purpose Probabilistic Programming System with Programmable Inference
PLDI Research Papers
Marco Cusumano-TownerMIT-CSAIL, Feras SaadMassachusetts Institute of Technology, Alexander K. LewMassachusetts Institute of Technology, USA, Vikash MansinghkaMIT
Media Attached
15:00 - 15:20
Incremental Precision-Preserving Symbolic Inference for Probabilistic Programs
PLDI Research Papers
Jieyuan ZhangUNSW, Australia, Jingling XueUNSW Sydney
Media Attached