Search Results for author: Saurabh Johri

Found 9 papers, 2 papers with code

Masking schemes for universal marginalisers

no code implementations pproximateinference AABI Symposium 2019 Divya Gautam, Maria Lomeli, Kostis Gourgoulias, Daniel H. Thompson, Saurabh Johri

We consider the effect of structure-agnostic and structure-dependent masking schemes when training a universal marginaliser (arXiv:1711. 00695) in order to learn conditional distributions of the form $P(x_i |\mathbf x_{\mathbf b})$, where $x_i$ is a given random variable and $\mathbf x_{\mathbf b}$ is some arbitrary subset of all random variables of the generative model of interest.


Leveraging directed causal discovery to detect latent common causes

no code implementations22 Oct 2019 Ciarán M. Lee, Christopher Hart, Jonathan G. Richens, Saurabh Johri

Here, we devise a general heuristic which takes a causal discovery algorithm that can only distinguish purely directed causal relations and modifies it to also detect latent common causes.

Causal Discovery Causal Inference +1

Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs

no code implementations16 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

Counterfactual diagnosis

1 code implementation15 Oct 2019 Jonathan G. Richens, Ciaran M. Lee, Saurabh Johri

We show that this approach is closer to the diagnostic reasoning of clinicians and significantly improves the accuracy and safety of the resulting diagnoses.

BIG-bench Machine Learning counterfactual +5

Universal Marginalizer for Amortised Inference and Embedding of Generative Models

no code implementations12 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.


A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis

no code implementations27 Jun 2018 Salman Razzaki, Adam Baker, Yura Perov, Katherine Middleton, Janie Baxter, Daniel Mullarkey, Davinder Sangar, Michael Taliercio, Mobasher Butt, Azeem Majeed, Arnold DoRosario, Megan Mahoney, Saurabh Johri

We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare favourably with human doctors with respect to triage and diagnostic accuracy.

A Universal Marginalizer for Amortized Inference in Generative Models

no code implementations2 Nov 2017 Laura Douglas, Iliyan Zarov, Konstantinos Gourgoulias, Chris Lucas, Chris Hart, Adam Baker, Maneesh Sahani, Yura Perov, Saurabh Johri

We consider the problem of inference in a causal generative model where the set of available observations differs between data instances.

Cannot find the paper you are looking for? You can Submit a new open access paper.