Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs

no code implementations • 16 Oct 2019 • Robert Walecki, Kostis Gourgoulias, Adam Baker, Chris Hart, Chris Lucas, Max Zwiessele, Albert Buchard, Maria Lomeli, Yura Perov, Saurabh Johri

Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty.

Probabilistic Programming

Universal Marginalizer for Amortised Inference and Embedding of Generative Models

no code implementations • 12 Nov 2018 • Robert Walecki, Albert Buchard, Kostis Gourgoulias, Chris Hart, Maria Lomeli, A. K. W. Navarro, Max Zwiessele, Yura Perov, Saurabh Johri

Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty.

Clustering

Differentially Private Gaussian Processes

no code implementations • 2 Jun 2016 • 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.

Gaussian Processes regression