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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

pldi-2017-papers
10:50 - 12:30: PLDI Research Papers - Learning and Probabilistic at Actes, Civil Engineering
Chair(s): Swarat ChaudhuriRice University
pldi-2017-papers149786220000010:50 - 11:15
Talk
Kuat YessenovMIT, Ivan KurajMIT CSAIL, USA, Armando Solar-LezamaMIT CSAIL
Media Attached
pldi-2017-papers149786370000011:15 - 11:40
Talk
Yaniv DavidTechnion, Nimrod PartushTechnion, Eran YahavTechnion
pldi-2017-papers149786520000011:40 - 12:05
Talk
Osbert BastaniStanford University, Rahul SharmaMicrosoft Research, Alex AikenStanford University, Percy LiangStanford University
Media Attached
pldi-2017-papers149786670000012:05 - 12:30
Talk
Daniel HuangHarvard University, Jean-Baptiste TristanOracle Labs, Greg MorrisettCornell University
Media Attached