490 citations · 1k across the 74 of their papers we have counts for
Showing 2014 · stat.MLShow all
2 papers · 2 filters
stat.ML2014★ 3 cited
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs
Yutian Chen, Vikash Mansinghka, Zoubin Ghahramani
Probabilistic programming languages can simplify the development of machine learning techniques, but only if inference is sufficiently scalable. Unfortunately, Bayesian parameter e…
stat.ML2014★ 11 cited
Variational Particle Approximations
Ardavan Saeedi, Tejas D Kulkarni, Vikash Mansinghka +1
Approximate inference in high-dimensional, discrete probabilistic models is a central problem in computational statistics and machine learning. This paper describes discrete partic…