5 papers
Data Provenance as Automatic Differentiation
Robert Atkey, Roly Perera
Automatic differentiation (AD) computes the derivative of a program alongside the program itself, as a linear map between tangent spaces, propagated forwards or backwards along an…
Literate Execution
Joe Bond, Jacob Pake, Cristina David +4
\emph{Literate programming}, introduced by Knurth, interleaves code and prose so that a program can be read as both executable and explanatory text. We propose \emph{literate execu…
AI-Assisted Authoring for Transparent, Data-Driven Documents
Alfonso Piscitelli, Cristina David, Mattia De Rosa +6
We introduce _transparent documents_, interactive web-based scholarly articles which allow readers to explore the relationship to the underlying data by hovering over fragments of…
Effect Handlers for Programmable Inference
Minh Nguyen, Roly Perera, Meng Wang +1
Inference algorithms for probabilistic programming are complex imperative programs with many moving parts. Efficient inference often requires customising an algorithm to a particul…
Modular Probabilistic Models via Algebraic Effects
Minh Nguyen, Roly Perera, Meng Wang +1
Probabilistic programming languages (PPLs) allow programmers to construct statistical models and then simulate data or perform inference over them. Many PPLs restrict models to a p…