7 citations · 12 across the 8 of their papers we have counts for
8 papers
Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing
Dominik Wagner, Basim Khajwal, C. -H. Luke Ong
It is well-known that the reparameterisation gradient estimator, which exhibits low variance in practice, is biased for non-differentiable models. This may compromise correctness o…
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support
Tim Reichelt, Luke Ong, Tom Rainforth
We introduce Support Decomposition Variational Inference (SDVI), a new variational inference (VI) approach for probabilistic programs with stochastic support. Existing approaches t…
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…
Fragments of ML Decidable by Nested Data Class Memory Automata
Conrad Cotton-Barratt, David Hopkins, Andrzej S. Murawski +1
The call-by-value language RML may be viewed as a canonical restriction of Standard ML to ground-type references, augmented by a "bad variable" construct in the sense of Reynolds.…
Decidable Models of Recursive Asynchronous Concurrency
Jonathan Kochems, C. -H. Luke Ong
Asynchronously communicating pushdown systems (ACPS) that satisfy the empty-stack constraint (a pushdown process may receive only when its stack is empty) are a popular decidable m…
Innocent Strategies are Sheaves over Plays---Deterministic, Non-deterministic and Probabilistic Innocence
Takeshi Tsukada, C. -H. Luke Ong
Although the HO/N games are fully abstract for PCF, the traditional notion of innocence (which underpins these games) is not satisfactory for such language features as non-determin…