Search Results for author:
Found 6 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.
A monadic solution to the Cartwright-Felleisen-Wadler conjecture
Models based on type-and-effect systems, in which there is a monad for every set of operations in the language, are.
Programming Languages
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.
On the Expressive Power of User-Defined Effects: Effect Handlers, Monadic Reflection, Delimited Control
We use the adequate finitary set-theoretic denotational semantics for the monadic calculus to show that effect handlers cannot be macro-expressed while preserving typeability either by monadic reflection or by delimited control.
Logic in Computer Science Programming Languages
No value restriction is needed for algebraic effects and handlers
We present a straightforward, sound Hindley-Milner polymorphic type system for algebraic effects and handlers in a call-by-value calculus, which allows type variable generalisation of arbitrary computations, not just values.
Programming Languages Logic in Computer Science
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).
