113 citations · 491 across the 32 of their papers we have counts for
4 papers · 1 filter
Denotational validation of higher-order Bayesian inference
Adam Ścibior, Ohad Kammar, Matthijs Vákár +7
We present a modular semantic account of Bayesian inference algorithms for probabilistic programming languages, as used in data science and machine learning. Sophisticated inferenc…
On Nesting Monte Carlo Estimators
Tom Rainforth, Robert Cornish, Hongseok Yang +2
Many problems in machine learning and statistics involve nested expectations and thus do not permit conventional Monte Carlo (MC) estimation. For such problems, one must nest estim…
Algebraic Laws for Weak Consistency (Extended Version)
Andrea Cerone, Alexey Gotsman, Hongseok Yang
Modern distributed systems often rely on so called weakly-consistent databases, which achieve scalability by sacrificing the consistency guarantee of distributed transaction proces…
A Convenient Category for Higher-Order Probability Theory
Chris Heunen, Ohad Kammar, Sam Staton +1
Higher-order probabilistic programming languages allow programmers to write sophisticated models in machine learning and statistics in a succinct and structured way, but step outsi…