113 citations · 491 across the 32 of their papers we have counts for
9 papers · 1 filter
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference
Wonyeol Lee, Xavier Rival, Hongseok Yang
We present a static analysis for discovering differentiable or more generally smooth parts of a given probabilistic program, and show how the analysis can be used to improve the pa…
Towards Verified Stochastic Variational Inference for Probabilistic Programs
Wonyeol Lee, Hangyeol Yu, Xavier Rival +1
Probabilistic programming is the idea of writing models from statistics and machine learning using program notations and reasoning about these models using generic inference engine…
The Beta-Bernoulli process and algebraic effects
Sam Staton, Dario Stein, Hongseok Yang +3
In this paper we use the framework of algebraic effects from programming language theory to analyze the Beta-Bernoulli process, a standard building block in Bayesian models. Our an…
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…
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…
Design and Implementation of Probabilistic Programming Language Anglican
David Tolpin, Jan Willem van de Meent, Hongseok Yang +1
Anglican is a probabilistic programming system designed to interoperate with Clojure and other JVM languages. We introduce the programming language Anglican, outline our design cho…
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints
Sam Staton, Hongseok Yang, Chris Heunen +2
We study the semantic foundation of expressive probabilistic programming languages, that support higher-order functions, continuous distributions, and soft constraints (such as Ang…
Abstraction Refinement Guided by a Learnt Probabilistic Model
Radu Grigore, Hongseok Yang
The core challenge in designing an effective static program analysis is to find a good program abstraction -- one that retains only details relevant to a given query. In this paper…
Two for the Price of One: Lifting Separation Logic Assertions
Jacob Thamsborg, Lars Birkedal, Hongseok Yang
Recently, data abstraction has been studied in the context of separation logic, with noticeable practical successes: the developed logics have enabled clean proofs of tricky challe…