Daniel Alabi

Artificial Intelligence · with Immorlica · 2018

A researcher in the Science corpus: 2 papers; trusts 18 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 13 citations into it; #21,934 of 35,345 by trust here, #25,809 by citations
• Safety, Robustness & Interpretability — 10 citations into it
• Safety Research — 8 citations into it
• AI Ethics, Fairness & Society — 6 citations into it
• Sociology and Political Science — 6 citations into it

Whom Daniel Alabi trusts overall

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

1. Adam Tauman Kalai
2. Michael Kearns
3. Cynthia Dwork
4. Martin Jaggi
5. Alekh Agarwal
6. Leslie G. Valiant
7. Santosh Srinivas Vempala
8. Robert E. Schapire
9. Alexandra Chouldechova
10. Yishay Mansour

Who trusts Daniel Alabi overall

1. Robert C. Williamson
2. Berk Ustun
3. Luca Oneto
4. Aditya Krishna Menon
5. Alekh Agarwal
6. Yang Liu
7. Simon Caton
8. Michele Donini

Papers from Daniel Alabi's perspective

Ordered by how strongly the researchers Daniel Alabi 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. Fairness through awareness
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
8. Attention is All you Need (Neural Information Processing Systems)
9. Inherent Trade-Offs in the Fair Determination of Risk Scores
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Unleashing Linear Optimizers for Group-Fair Learning and Optimization (2018) — 28 citations
• When optimizing nonlinear objectives is no harder than linear objectives (2018) — 5 citations