Samuel Jenkins

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 6 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #17,314 of 35,345 by trust here, #12,387 by citations
• Safety, Robustness & Interpretability — 29 citations into it; #1,134 of 2,251 by trust here, #1,089 by citations
• Health Informatics — 8 citations into it

Whom Samuel Jenkins trusts overall

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

1. Yin Lou
2. R. Caruana
3. Scott M. Lundberg
4. Trevor J. Hastie
5. Cynthia Rudin
6. Jerome H. Friedman
7. D. Norman
8. Robert Tibshirani
9. Johannes Gehrke

Who trusts Samuel Jenkins overall

1. Cynthia Rudin
2. Forough Poursabzi-Sangdeh
3. Gesina Schwalbe
4. Maithra Raghu
5. Dena F. Mujtaba
6. Christoph Molnar
7. Ashraf Abdul
8. Michael A. Madaio
9. Rishabh Agarwal

Papers from Samuel Jenkins's perspective

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

1. Scikit-learn: Machine Learning in Python
2. Greedy function approximation: A gradient boosting machine.
3. ImageNet classification with deep convolutional neural networks
4. Towards A Rigorous Science of Interpretable Machine Learning
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. The mythos of model interpretability
7. A Unified Approach to Interpreting Model Predictions
8. Regression Shrinkage and Selection via the Lasso
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Accurate intelligible models with pairwise interactions

Their papers

• InterpretML: A Unified Framework for Machine Learning Interpretability (2019) — 655 citations
• Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning (2020) — 609 citations