10 citations · 35 across the 9 of their papers we have counts for
4 papers · 1 filter
Transforming Probabilistic Programs for Model Checking
Ryan Bernstein, Matthijs Vákár, Jeannette Wing
Probabilistic programming is perfectly suited to reliable and transparent data science, as it allows the user to specify their models in a high-level language without worrying abou…
Reverse AD at Higher Types: Pure, Principled and Denotationally Correct
Matthijs Vákár
We show how to define forward- and reverse-mode automatic differentiation source-code transformations or on a standard higher-order functional language. The transformations generat…
Denotational Correctness of Forward-Mode Automatic Differentiation for Iteration and Recursion
Matthijs Vákár
We present semantic correctness proofs of forward-mode Automatic Differentiation (AD) for languages with sources of partiality such as partial operations, lazy conditionals on real…
Correctness of Automatic Differentiation via Diffeologies and Categorical Gluing
Mathieu Huot, Sam Staton, Matthijs Vákár
We present semantic correctness proofs of Automatic Differentiation (AD). We consider a forward-mode AD method on a higher order language with algebraic data types, and we characte…