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Found 7 papers, 1 papers with code
Recalibrating classifiers for interpretable abusive content detection
We investigate the use of machine learning classifiers for detecting online abuse in empirical research.
$ω$PAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs
We introduce a new setting, the category of $\omega$PAP spaces, for reasoning denotationally about expressive differentiable and probabilistic programming languages.
LazyPPL: laziness and types in non-parametric probabilistic programs
We introduce LazyPPL, a prototype probabilistic programming library for Haskell.
Compositional Semantics for Probabilistic Programs with Exact Conditioning
We define a probabilistic programming language for Gaussian random variables with a first-class exact conditioning construct.
A Convenient Category for Higher-Order Probability Theory
Higher-order probabilistic programming languages allow programmers to write sophisticated models in machine learning and statistics in a succinct and structured way, but step outside the standard measure-theoretic formalization of probability theory.
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints
We study the semantic foundation of expressive probabilistic programming languages, that support higher-order functions, continuous distributions, and soft constraints (such as Anglican, Church, and Venture).
Relating coalgebraic notions of bisimulation
The theory of coalgebras, for an endofunctor on a category, has been proposed as a general theory of transition systems.
Logic in Computer Science F.3.2, G.2.m
