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Ahmed M. Alaa
Ahmed M. Alaa
Assistant Professor, UC Berkeley and UCSF
Verified email at berkeley.edu
Title
Cited by
Cited by
Year
Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants
AM Alaa, T Bolton, E Di Angelantonio, JHF Rudd, M Van der Schaar
PloS one 14 (5), e0213653, 2019
4072019
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
AM Alaa, M van der Schaar
Advances in Neural Information Processing Systems (NeurIPS), 3424-3432, 2017
3102017
From real‐world patient data to individualized treatment effects using machine learning: Current and future methods to address underlying challenges
I Bica, AM Alaa, C Lambert, M van der Schaar
Clinical Pharmacology and Therapeutics, 2020
1612020
How artificial intelligence and machine learning can help healthcare systems respond to COVID-19
M Van der Schaar, AM Alaa, A Floto, A Gimson, S Scholtes, A Wood, ...
Machine Learning 110, 1-14, 2021
1502021
Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design
A Alaa, M Schaar
International Conference on Machine Learning (ICML), 129-138, 2018
1442018
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations
I Bica, AM Alaa, J Jordon, M van der Schaar
International Conference on Learning Representations (ICLR), 2020
1432020
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks
B Lim, A Alaa, M van der Schaar
Advances in Neural Information Processing Systems (NeurIPS), 7493-7503, 2018
1192018
How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models
AM Alaa, B van Breugel, E Saveliev, M van der Schaar
International Conference on Machine Learning (ICML), 2022
1152022
Attentive state-space modeling of disease progression
AM Alaa, M van der Schaar
Advances in Neural Information Processing Systems (NeurIPS), 11334-11344, 2019
1112019
Deep multi-task gaussian processes for survival analysis with competing risks
AM Alaa, M van der Schaar
Advances in Neural Information Processing Systems (NeurIPS), 2326-2334, 2017
1082017
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
AM Alaa, M van der Schaar
International Conference on Machine Learning (ICML), 2018
982018
Deep Counterfactual Networks with Propensity-Dropout
AM Alaa, M Weisz, M van der Schaar
ICML 2017 - Workshop on Principled Approaches to Deep Learning, 2017
972017
Demystifying Black-box Models with Symbolic Metamodels
AM Alaa, M van der Schaar
Advances in Neural Information Processing Systems (NeurIPS), 11301-11311, 2019
922019
Lifelong Bayesian Optimization
Y Zhang, J Jordon, AM Alaa, M van der Schaar
arXiv preprint arXiv:1905.12280, 2019
91*2019
Validating Causal Inference Models via Influence Functions
A Alaa, M Van Der Schaar
International Conference on Machine Learning (ICML), 191-201, 2019
872019
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
I Bica, AM Alaa, M van der Schaar
International Conference on Machine Learning (ICML), 2020
822020
Conformal Time-series Forecasting
K Stankevičiūtė, A Alaa, M van der Schaar
Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS), 2021
79*2021
Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes
AM Alaa, J Yoon, S Hu, M van der Schaar
IEEE Transactions on Biomedical Engineering, 2016
782016
Prognostication and risk factors for cystic fibrosis via automated machine learning
AM Alaa, M van der Schaar
Nature Scientific reports 8 (1), 1-19, 2018
672018
Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation
J Yoon, WR Zame, A Banerjee, M Cadeiras, AM Alaa, M van der Schaar
PloS one 13 (3), e0194985, 2018
642018
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