Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling

no code implementations • 14 Jul 2019 • Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle, Martin C. Rinard, Vikash K. Mansinghka

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.

Probabilistic Programming Time Series +1

Time Series Structure Discovery via Probabilistic Program Synthesis

no code implementations • 21 Nov 2016 • Ulrich Schaechtle, Feras Saad, Alexey Radul, Vikash Mansinghka

There is a widespread need for techniques that can discover structure from time series data.

Clustering Program Synthesis +2

Probabilistic Programming with Gaussian Process Memoization

no code implementations • 17 Dec 2015 • Ulrich Schaechtle, Ben Zinberg, Alexey Radul, Kostas Stathis, Vikash K. Mansinghka

Gaussian Processes (GPs) are widely used tools in statistics, machine learning, robotics, computer vision, and scientific computation.

Bayesian Optimization Gaussian Processes +4