90 citations · 428 across the 38 of their papers we have counts for
8 papers · 1 filter
Estimating Structural Target Functions using Machine Learning and Influence Functions
Alicia Curth, Ahmed M. Alaa, Mihaela van der Schaar
We aim to construct a class of learning algorithms that are of practical value to applied researchers in fields such as biostatistics, epidemiology and econometrics, where the need…
CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19
Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar
The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these…
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Ahmed M. Alaa, Mihaela van der Schaar
Deep learning models achieve high predictive accuracy across a broad spectrum of tasks, but rigorously quantifying their predictive uncertainty remains challenging. Usable estimate…
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
Ahmed M. Alaa, Mihaela van der Schaar
Recurrent neural networks (RNNs) are instrumental in modelling sequential and time-series data. Yet, when using RNNs to inform decision-making, predictions by themselves are not su…
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Alex J. Chan, Ahmed M. Alaa, Zhaozhi Qian +1
Modern neural networks have proven to be powerful function approximators, providing state-of-the-art performance in a multitude of applications. They however fall short in their ab…
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar
The coronavirus disease 2019 (COVID-19) global pandemic has led many countries to impose unprecedented lockdown measures in order to slow down the outbreak. Questions on whether go…
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations
Ioana Bica, Ahmed M. Alaa, James Jordon +1
Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions. In this paper, w…
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Zhaozhi Qian, Ahmed M. Alaa, Alexis Bellot +2
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has,…