Search Results for author:
Found 9 papers, 2 papers with code
Learning medical triage from clinicians using Deep Q-Learning
We show that this approach is on a par with human performance, yielding safe triage decisions in 94% of cases, and matching expert decisions in 85% of cases.
Masking schemes for universal marginalisers
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
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.
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference.
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty.
Counterfactual diagnosis
We show that this approach is closer to the diagnostic reasoning of clinicians and significantly improves the accuracy and safety of the resulting diagnoses.
Universal Marginalizer for Amortised Inference and Embedding of Generative Models
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
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
We consider the problem of inference in a causal generative model where the set of available observations differs between data instances.
