7 citations · 8 across the 3 of their papers we have counts for
3 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…
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…
Differentially Private Gaussian Processes
Michael Thomas Smith, Max Zwiessele, Neil D. Lawrence
A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the provable privacy guarantees of the di…