Aaron Fisher

Artificial Intelligence · 2018

A researcher in the Science corpus: 3 papers; trusts 41 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #16,405 of 35,345 by trust here, #20,377 by citations
• Statistics and Probability — 36 citations into it
• Safety, Robustness & Interpretability — 16 citations into it; #738 of 2,251 by trust here, #1,186 by citations
• Sociology and Political Science — 14 citations into it
• Evaluation & Benchmarks — 8 citations into it; #1,513 of 3,147 by trust here, #2,393 by citations
• Statistical Learning Theory — 8 citations into it
• Molecular Biology — 8 citations into it
• Nature and Landscape Conservation — 8 citations into it

Whom Aaron Fisher trusts overall

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

1. Leo Breiman
2. Giles Hooker
3. Cynthia Rudin
4. Baptiste Gregorutti
5. M. J. van der Laan
6. Lucas K. Mentch
7. Kellie J. Archer
8. Malu Luz Calle
9. Hugh A. Chipman
10. Bertrand Michel

Who trusts Aaron Fisher overall

1. Cynthia Rudin
2. Jiayun Dong
3. Lesia Semenova
4. Nadia Burkart
5. Xinru Wang
6. Maximilian Pichler
7. Christoph Molnar
8. Amanda Coston
9. Davinder Kaur
10. Giuseppe Casalicchio

Papers from Aaron Fisher's perspective

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

1. Random Forests
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Regression Shrinkage and Selection via the Lasso
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
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

• All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously (2018) — 1,742 citations
• Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the "Rashomon" Perspective (2018) — 187 citations
• All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance (2018) — 128 citations