Ahmed M. Alaa | DeepAI

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Featured Co-authors

Mihaela van der Schaar
137 publications
David Sontag
74 publications
Yao Zhang
67 publications
Can Xu
48 publications
Jinsung Yoon
30 publications
Zhaozhi Qian
19 publications
Ioana Bica
15 publications
Alicia Curth
14 publications
James Jordon
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Alexis Bellot
12 publications
Zeshan Hussain
10 publications

∙ 05/08/2023

Large-Scale Study of Temporal Shift in Health Insurance Claims

Most machine learning models for predicting clinical outcomes are develo...

0 Christina X. Ji, et al. ∙

∙ 04/04/2023

Conformalized Unconditional Quantile Regression

We develop a predictive inference procedure that combines conformal pred...

0 Ahmed M. Alaa, et al. ∙

∙ 02/17/2021

How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models

Devising domain- and model-agnostic evaluation metrics for generative mo...

7 Ahmed M. Alaa, et al. ∙

∙ 01/28/2021

Learning Matching Representations for Individualized Organ Transplantation Allocation

Organ transplantation is often the last resort for treating end-stage il...

4 Can Xu, et al. ∙

∙ 08/14/2020

Semiparametric Estimation and Inference on Structural Target Functions using Machine Learning and Influence Functions

We aim to construct a class of learning algorithms that are of practical...

15 Alicia Curth, et al. ∙

∙ 07/27/2020

CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19

The coronavirus disease 2019 (COVID-19) global pandemic poses the threat...

23 Zhaozhi Qian, et al. ∙

∙ 06/26/2020

Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift

Modern neural networks have proven to be powerful function approximators...

5 Alex J. Chan, et al. ∙

∙ 06/20/2020

Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions

Recurrent neural networks (RNNs) are instrumental in modelling sequentia...

4 Ahmed M. Alaa, et al. ∙

∙ 05/13/2020

When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes

The coronavirus disease 2019 (COVID-19) global pandemic has led many cou...

0 Zhaozhi Qian, et al. ∙

∙ 05/13/2020

When to Lift the Lockdown? Global COVID-19 Scenario Planning and Policy Effects using Compartmental Gaussian Processes

The coronavirus disease 2019 (COVID-19) outbreak has led government offi...

0 Zhaozhi Qian, et al. ∙

∙ 02/10/2020

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations

Identifying when to give treatments to patients and how to select among ...

0 Ioana Bica, et al. ∙

∙ 01/08/2020

Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes

Comorbid diseases co-occur and progress via complex temporal patterns th...

0 Zhaozhi Qian, et al. ∙

∙ 05/29/2019

Lifelong Bayesian Optimization

Automatic Machine Learning (Auto-ML) systems tackle the problem of autom...

0 Yao Zhang, et al. ∙

∙ 02/01/2019

Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders

The estimation of treatment effects is a pervasive problem in medicine. ...

0 Ioana Bica, et al. ∙

∙ 10/24/2018

Forecasting Individualized Disease Trajectories using Interpretable Deep Learning

Disease progression models are instrumental in predicting individual-lev...

0 Ahmed M. Alaa, et al. ∙

∙ 02/20/2018

AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning

Clinical prognostic models derived from largescale healthcare data can i...

0 Ahmed M. Alaa, et al. ∙

∙ 12/24/2017

Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms

We investigate the problem of estimating the causal effect of a treatmen...

0 Ahmed M. Alaa, et al. ∙

∙ 06/19/2017

Deep Counterfactual Networks with Propensity-Dropout

We propose a novel approach for inferring the individualized causal effe...

0 Ahmed M. Alaa, et al. ∙

∙ 12/18/2016

A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference

Modeling continuous-time physiological processes that manifest a patient...

0 Ahmed M. Alaa, et al. ∙

∙ 11/16/2016

A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data

Critically ill patients in regular wards are vulnerable to unanticipated...

0 Ahmed M. Alaa, et al. ∙

∙ 10/27/2016

Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes

Objective: In this paper, we develop a personalized real-time risk scori...

0 Ahmed M. Alaa, et al. ∙

∙ 10/24/2016

Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition

We develop a Bayesian model for decision-making under time pressure with...

0 Ahmed M. Alaa, et al. ∙

∙ 05/03/2016

Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts

We develop a personalized real time risk scoring algorithm that provides...

0 Ahmed M. Alaa, et al. ∙