12 citations · 20 across the 4 of their papers we have counts for
4 papers
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing
Basim Khajwal, C. -H. Luke Ong, Dominik Wagner
We study the foundations of variational inference, which frames posterior inference as an optimisation problem, for probabilistic programming. The dominant approach for optimisatio…
Initial Limit Datalog: a New Extensible Class of Decidable Constrained Horn Clauses
Toby Cathcart Burn, Luke Ong, Steven Ramsay +1
We present initial limit Datalog, a new extensible class of constrained Horn clauses for which the satisfiability problem is decidable. The class may be viewed as a generalisation…
Densities of Almost Surely Terminating Probabilistic Programs are Differentiable Almost Everywhere
Carol Mak, C. -H. Luke Ong, Hugo Paquet +1
We study the differential properties of higher-order statistical probabilistic programs with recursion and conditioning. Our starting point is an open problem posed by Hongseok Yan…
HoCHC: A Refutationally Complete and Semantically Invariant System of Higher-order Logic Modulo Theories
C. -H. Luke Ong, Dominik Wagner
We present a simple resolution proof system for higher-order constrained Horn clauses (HoCHC) - a system of higher-order logic modulo theories - and prove its soundness and refutat…