Amanda Coston

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 46 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it
• Safety, Robustness & Interpretability — 32 citations into it
• Safety Research — 27 citations into it
• AI Ethics, Fairness & Society — 23 citations into it
• Statistics and Probability — 18 citations into it
• Sociology and Political Science — 13 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Causal Inference & Discovery — 7 citations into it

Whom Amanda Coston trusts overall

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

1. Cynthia Rudin
2. James M. Robins
3. Alexandra Chouldechova
4. Faisal Kamiran
5. Miroslav Dud'ik
6. Edward H. Kennedy
7. Moritz Hardt
8. Alekh Agarwal
9. Masashi Sugiyama
10. Solon Barocas

Who trusts Amanda Coston overall

1. Shira Mitchell
2. Ashesh Rambachan
3. Atoosa Kasirzadeh
4. Lucas Rosenblatt

Papers from Amanda Coston's perspective

Ordered by how strongly the researchers Amanda Coston 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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

• Characterizing Fairness Over the Set of Good Models Under Selective Labels (2021) — 101 citations
• Fair Transfer Learning with Missing Protected Attributes (2019) — 123 citations
• Counterfactual risk assessments, evaluation, and fairness (2019) — 135 citations