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POPL 2019
Sun 13 - Sat 19 January 2019 Cascais, Portugal
Wed 16 Jan 2019 14:29 - 14:51 at Sala I - Probabilistic Programming and Semantics Chair(s): Justin Hsu

We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-specific data modeling languages that capture key properties of a broad class of data generating processes, using Bayesian inference to synthesize probabilistic programs in these modeling languages given observed data. We provide a precise formulation of Bayesian synthesis for automatic data modeling that identifies sufficient conditions for the resulting synthesis procedure to be sound. We also derive a general class of synthesis algorithms for domain-specific languages specified by probabilistic context-free grammars and establish the soundness of our approach for these languages. We apply the techniques to automatically synthesize probabilistic programs for time series data and multivariate tabular data. We show how to analyze the structure of the synthesized programs to compute, for key qualitative properties of interest, the probability that the underlying data generating process exhibits each of these properties. Second, we translate probabilistic programs in the domain-specific language into probabilistic programs in Venture, a general-purpose probabilistic programming system. The translated Venture programs are then executed to obtain predictions of new time series data and new multivariate data records. Experimental results show that our techniques can accurately infer qualitative structure in multiple real-world data sets and outperform standard data analysis methods in forecasting and predicting new data.

Slide Deck (popl19main-p184-slides.pdf)3.25MiB

Wed 16 Jan
Times are displayed in time zone: Greenwich Mean Time : Belfast change

13:45 - 14:51: Probabilistic Programming and SemanticsResearch Papers at Sala I
Chair(s): Justin HsuUniversity of Wisconsin-Madison, USA
13:45 - 14:07
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
Research Papers
Maria I. GorinovaThe University of Edinburgh, Andrew D. GordonMicrosoft Research and University of Edinburgh, Charles SuttonUniversity of Edinburgh
Link to publication DOI Pre-print Media Attached File Attached
14:07 - 14:29
A Domain Theory for Statistical Probabilistic ProgrammingDistinguished Paper
Research Papers
Matthijs VákárUniversity of Oxford, Ohad KammarUniversity of Edinburgh, Sam StatonUniversity of Oxford
Link to publication DOI Pre-print Media Attached File Attached
14:29 - 14:51
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
Research Papers
Feras SaadMassachusetts Institute of Technology, Marco Cusumano-TownerMIT-CSAIL, Ulrich SchaechtleMassachusetts Institute of Technology, USA, Martin RinardMassachusetts Institute of Technology, Vikash MansinghkaMIT
Link to publication DOI Media Attached File Attached