12 citations · 17 across the 4 of their papers we have counts for
4 papers
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Robert Walecki, Kostis Gourgoulias, Adam Baker +7
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty. Inference…
Leveraging directed causal discovery to detect latent common causes
Ciarán M. Lee, Christopher Hart, Jonathan G. Richens +1
The discovery of causal relationships is a fundamental problem in science and medicine. In recent years, many elegant approaches to discovering causal relationships between two var…
Universal Marginalizer for Amortised Inference and Embedding of Generative Models
Robert Walecki, Albert Buchard, Kostis Gourgoulias +6
Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty. There exist a considerable nu…
A Universal Marginalizer for Amortized Inference in Generative Models
Laura Douglas, Iliyan Zarov, Konstantinos Gourgoulias +6
We consider the problem of inference in a causal generative model where the set of available observations differs between data instances. We show how combining samples drawn from t…