Ahmed M. Alaa

Statistics and Probability · 2017–2021

A researcher in the Science corpus: 12 papers; trusts 325 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 156 citations into it; #2,651 of 12,932 by trust here, #2,965 by citations
• Artificial Intelligence — 143 citations into it; #7,744 of 35,345 by trust here, #11,072 by citations
• Causal Inference & Discovery — 73 citations into it; #131 of 364 by trust here, #151 by citations
• Probabilistic & Bayesian Methods — 45 citations into it
• Computer Vision and Pattern Recognition — 44 citations into it
• Generative Models — 28 citations into it
• Management Science and Operations Research — 24 citations into it
• Optimization & Training — 22 citations into it

Whom Ahmed M. Alaa trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. James M. Robins
2. Donald B. Rubin
3. Jennifer L. Hill
4. Diederik P. Kingma
5. Uri Shalit
6. Susan Athey
7. Stefan Wager
8. Fredrik D. Johansson
9. Yoshua Bengio
10. Geoffrey E. Hinton

Who trusts Ahmed M. Alaa overall

1. Ioana Bica
2. Mihaela van der Schaar
3. James Jordon
4. Bryan Lim
5. Adarsh Subbaswamy
6. Sameer Quazi
7. Liuyi Yao
8. Individualized Treat
9. Bernhard Schölkopf
10. Amanda Coston

Papers from Ahmed M. Alaa's perspective

Ordered by how strongly the researchers Ahmed M. Alaa trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020) — 241 citations
• Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms (2017) — 50 citations
• Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019) — 138 citations
• Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes (2017) — 346 citations
• Deep Counterfactual Networks with Propensity-Dropout (2017) — 94 citations
• From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges (2020) — 219 citations
• How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models (2021) — 318 citations
• How artificial intelligence and machine learning can help healthcare systems respond to COVID-19 (2020) — 188 citations
• Attentive State-Space Modeling of Disease Progression (2019) — 130 citations
• Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants (2019) — 490 citations
• AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning (2018) — 103 citations
• Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design (2018) — 151 citations