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Mon 19 Jun 2017 12:05 - 12:30 at Actes, Civil Engineering - Learning and Probabilistic Chair(s): Swarat Chaudhuri

The job of a typical compiler is to convert a program written in some source language into assembly code. In contrast, a probabilistic programming language (PPL) compiler converts a specification of a probabilistic model into a statistical inference algorithm. Hence, the requirements of such a compiler are markedly different from those for a conventional one. In this paper, we describe a compiler for a restricted PPL that expresses Bayesian networks. In particular, we present a sequence of intermediate languages (ILs) that guide a compiler in gradually and successively refining a declarative specification of a probabilistic model into an executable Markov Chain Monte Carlo (MCMC) inference algorithm. The compilation strategy produces \emph{composable} MCMC algorithms for execution on a CPU or GPU.

Mon 19 Jun

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

10:50 - 12:30
Learning and ProbabilisticPLDI Research Papers at Actes, Civil Engineering
Chair(s): Swarat Chaudhuri Rice University
10:50
25m
Talk
DemoMatch: API Discovery from Demonstrations
PLDI Research Papers
Kuat Yessenov MIT, Ivan Kuraj MIT CSAIL, USA, Armando Solar-Lezama MIT CSAIL
Media Attached
11:15
25m
Talk
Similarity of Binaries through re-Optimization
PLDI Research Papers
Yaniv David Technion, Nimrod Partush Technion, Eran Yahav Technion
11:40
25m
Talk
Synthesizing Program Input Grammars
PLDI Research Papers
Osbert Bastani Stanford University, Rahul Sharma Microsoft Research, Alex Aiken Stanford University, Percy Liang Stanford University
Media Attached
12:05
25m
Talk
Compiling Markov Chain Monte Carlo Algorithms for Probabilistic Modeling
PLDI Research Papers
Daniel Huang Harvard University, Jean-Baptiste Tristan Oracle Labs, Greg Morrisett Cornell University
Media Attached