A Gradual Probabilistic Lambda Calculus
Wenjia Ye, Mat\'ias Toro, Federico Olmedo

TL;DR
This paper introduces GPLC, a gradual probabilistic lambda calculus that combines static and dynamic typing with probabilistic features, ensuring type safety and smooth type transitions.
Contribution
It formalizes GPLC, integrating gradual typing with probabilistic programming, and proves its type safety and gradual guarantee properties.
Findings
GPLC supports gradual introduction/removal of static and probabilistic annotations.
The static semantics relies on probabilistic couplings for defining relations.
GPLC is proven to be type safe and satisfies the gradual guarantee.
Abstract
Probabilistic programming languages have recently gained a lot of attention, in particular due to their applications in domains such as machine learning and differential privacy. To establish invariants of interest, many such languages include some form of static checking in the form of type systems. However, adopting such a type discipline can be cumbersome or overly conservative. Gradual typing addresses this problem by supporting a smooth transition between static and dynamic checking, and has been successfully applied for languages with different constructs and type abstractions. Nevertheless, its benefits have never been explored in the context of probabilistic languages. In this work, we present and formalize GPLC, a gradual source probabilistic lambda calculus. GPLC includes a binary probabilistic choice operator and allows programmers to gradually introduce/remove static…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
