Phebe Vayanos

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 51 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it
• Safety, Robustness & Interpretability — 16 citations into it
• Information Systems — 14 citations into it
• Algorithms & Complexity — 10 citations into it
• Statistical Learning Theory — 9 citations into it; #603 of 1,038 by trust here, #678 by citations
• Sociology and Political Science — 9 citations into it; #6,010 of 22,756 by trust here, #10,132 by citations
• Statistics and Probability — 9 citations into it; #4,896 of 12,932 by trust here, #5,294 by citations
• AI Ethics, Fairness & Society — 8 citations into it; #150 of 494 by trust here, #271 by citations

Whom Phebe Vayanos trusts overall

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

1. Dimitris J. Bertsimas
2. J. Ross Quinlan
3. Faisal Kamiran
4. Sicco Verwer
5. Leo Breiman
6. Cynthia Rudin
7. Yin Lou
8. Toon Calders
9. Rahul Mazumder
10. Himabindu Lakkaraju

Who trusts Phebe Vayanos overall

1. Sina Aghaei
2. Emir Demirovi'c
3. Haoran Zhu
4. Adam N. Elmachtoub
5. Emilio Carrizosa
6. Vinícius G. Costa

Papers from Phebe Vayanos's perspective

Ordered by how strongly the researchers Phebe Vayanos 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. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making (2019) — 192 citations
• Partially Generative Neural Networks for Gang Crime Classification with Partial Information (2018) — 16 citations
• Learning Optimal Classification Trees: Strong Max-Flow Formulations (2020) — 28 citations
• Designing Fair, Efficient, and Interpretable Policies for Prioritizing Homeless Youth for Housing Resources (2018) — 65 citations