Mihaela van der Schaar

Artificial Intelligence · 2001–2024

A researcher in the Science corpus: 32 papers; trusts 702 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 300 citations into it; #4,593 of 35,345 by trust here, #7,241 by citations
• Statistics and Probability — 234 citations into it; #1,853 of 12,932 by trust here, #1,781 by citations
• Computer Vision and Pattern Recognition — 114 citations into it; #9,331 of 21,575 by trust here, #13,542 by citations
• Causal Inference & Discovery — 109 citations into it; #90 of 364 by trust here, #87 by citations
• Probabilistic & Bayesian Methods — 71 citations into it
• Representation & Self-Supervised Learning — 59 citations into it
• Management Science and Operations Research — 56 citations into it; #11,176 of 11,946 by trust here, #6,563 by citations
• Optimization & Training — 50 citations into it

Whom Mihaela van der Schaar trusts overall

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

1. Donald B. Rubin
2. Diederik P. Kingma
3. James M. Robins
4. Jennifer L. Hill
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Ahmed M. Alaa
8. Alex Krizhevsky
9. Ilya Sutskever
10. Susan Athey

Who trusts Mihaela van der Schaar overall

1. Ioana Bica
2. Ahmed M. Alaa
3. Bryan Lim
4. James Jordon
5. Liuyi Yao
6. Bernhard Schölkopf
7. Jinsung Yoon
8. Adarsh Subbaswamy
9. Zhixuan Chu
10. Joseph D. Futoma

Papers from Mihaela van der Schaar's perspective

Ordered by how strongly the researchers Mihaela van der Schaar 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• GAIN: Missing Data Imputation using Generative Adversarial Nets (2018) — 1,410 citations
• Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks (2018) — 76 citations
• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020) — 241 citations
• Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks (2017) — 340 citations
• Machine Learning in the Air (2019) — 209 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
• Cross-layer wireless multimedia transmission: challenges, principles, and new paradigms (2005) — 579 citations
• Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes (2017) — 346 citations
• Learning Optimal Policies from Observational Data (2018) — 20 citations
• Deep Counterfactual Networks with Propensity-Dropout (2017) — 94 citations
• Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning (2018) — 20 citations
• Reputation-based incentive protocols in crowdsourcing applications (2011) — 301 citations
• Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks (2020) — 128 citations
• Disease-Atlas: Navigating Disease Trajectories using Deep Learning (2018) — 39 citations
• From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges (2020) — 219 citations
• DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks (2018) — 819 citations
• Discovery and Clinical Decision Support for Personalized Healthcare (2017) — 79 citations
• Causal machine learning for predicting treatment outcomes (2024) — 334 citations
• How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models (2021) — 318 citations