Jamie H. Morgenstern

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 14 papers; trusts 142 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 140 citations into it; #6,495 of 35,345 by trust here, #2,287 by citations
• Safety Research — 86 citations into it; #2,636 of 5,829 by trust here, #707 by citations
• Safety, Robustness & Interpretability — 80 citations into it; #654 of 2,251 by trust here, #557 by citations
• AI Ethics, Fairness & Society — 69 citations into it; #169 of 494 by trust here, #45 by citations
• Sociology and Political Science — 44 citations into it
• Information Systems — 26 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it; #14,451 of 21,575 by trust here, #13,815 by citations
• Management Science and Operations Research — 25 citations into it; #1,281 of 11,946 by trust here, #100 by citations

Whom Jamie H. Morgenstern trusts overall

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

1. Kaiming He
2. Cynthia Dwork
3. Michael Kearns
4. Moritz Hardt
5. Alexandra Chouldechova
6. Matthew Joseph
7. Geoffrey E. Hinton
8. Shaoqing Ren
9. Lihong Li
10. Ross B. Girshick

Who trusts Jamie H. Morgenstern overall

1. Michael Kearns
2. Aaron Roth
3. Dana Pessach
4. Shira Mitchell
5. Luca Oneto
6. Emily L. Denton
7. Sarah Wiegreffe
8. Dan Hendrycks
9. Jack Bandy
10. Rowan Zellers

Papers from Jamie H. Morgenstern's perspective

Ordered by how strongly the researchers Jamie H. Morgenstern trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Datasheets for datasets (2018) — 3,178 citations
• Predictive Inequity in Object Detection (2019) — 244 citations
• Fairness in Learning: Classic and Contextual Bandits (2016) — 514 citations
• Fairness in Reinforcement Learning (2016) — 234 citations
• Diversity and Inclusion Metrics in Subset Selection (2020) — 95 citations
• On the Compatibility of Privacy and Fairness (2019) — 181 citations
• Meritocratic Fairness for Infinite and Contextual Bandits (2018) — 49 citations
• Fairness Incentives for Myopic Agents (2017) — 47 citations
• Fair Algorithms for Infinite and Contextual Bandits (2016) — 59 citations
• Defining and Designing Fair Algorithms (2018) — 8 citations
• The Price of Fair PCA: One Extra Dimension (2018) — 182 citations
• A Convex Framework for Fair Regression (2017) — 379 citations
• Rawlsian Fairness for Machine Learning (2016) — 88 citations
• Fair Learning in Markovian Environments (2016) — 35 citations