48 citations · 174 across the 34 of their papers we have counts for
34 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…
CycleQ: An Efficient Basis for Cyclic Equational Reasoning
Eddie Jones, C-. H. Luke Ong, Steven Ramsay
We propose a new cyclic proof system for automated, equational reasoning about the behaviour of pure functional programs. The key to the system is the way in which cyclic proof and…
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently
Tim Reichelt, Adam Goliński, Luke Ong +1
We show that the standard computational pipeline of probabilistic programming systems (PPSs) can be inefficient for estimating expectations and introduce the concept of expectation…
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…
Supermartingales, Ranking Functions and Probabilistic Lambda Calculus
Andrew Kenyon-Roberts, Luke Ong
We introduce a method for proving almost sure termination in the context of lambda calculus with continuous random sampling and explicit recursion, based on ranking supermartingale…
Verifying Liveness Properties of ML Programs
M. M. Lester, R. P. Neatherway, C. -H. L. Ong +1
Higher-order recursion schemes are a higher-order analogue of Boolean Programs; they form a natural class of abstractions for functional programs. We present a new, efficient algor…