Úrsula Hébert-Johnson

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 30 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it
• Safety, Robustness & Interpretability — 18 citations into it
• AI Ethics, Fairness & Society — 14 citations into it
• Safety Research — 13 citations into it
• Management Science and Operations Research — 9 citations into it
• Statistical Learning Theory — 8 citations into it
• Computer Networks and Communications — 6 citations into it
• Sociology and Political Science — 6 citations into it

Whom Úrsula Hébert-Johnson trusts overall

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

1. Moritz Hardt
2. Cynthia Dwork
3. Vitaly Feldman
4. Avrim Blum
5. Alexandra Chouldechova
6. Jonathan Ullman
7. Michael Kearns
8. Shai Shalev-Shwartz
9. Adam Tauman Kalai
10. Elad Hazan

Who trusts Úrsula Hébert-Johnson overall

1. Michael Kearns
2. Luca Oneto
3. Michael P. Kim
4. Shira Mitchell
5. Berk Ustun
6. Cynthia Dwork
7. Elliot Creager
8. Aaron Roth
9. Angelina Wang
10. Guy N. Rothblum

Papers from Úrsula Hébert-Johnson's perspective

Ordered by how strongly the researchers Úrsula Hébert-Johnson 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. Gradient-based learning applied to document recognition
4. Learning Multiple Layers of Features from Tiny Images
5. ImageNet: A large-scale hierarchical image database
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Fairness through awareness
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Calibration for the (Computationally-Identifiable) Masses (2017) — 92 citations
• Multicalibration: Calibration for the (Computationally-Identifiable) Masses (2018) — 504 citations