Jack Dunn

Statistics and Probability · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 71 researchers and is trusted by 75 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 50 citations into it; #10,716 of 12,932 by trust here, #9,714 by citations
• Artificial Intelligence — 37 citations into it; #2,872 of 35,345 by trust here, #10,798 by citations
• Statistical Learning Theory — 15 citations into it
• Algorithms & Complexity — 11 citations into it; #58 of 516 by trust here, #144 by citations
• Causal Inference & Discovery — 11 citations into it
• Management Science and Operations Research — 10 citations into it
• Optimization & Training — 9 citations into it
• Mathematical Optimization — 7 citations into it

Whom Jack Dunn trusts overall

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

1. Leo Breiman
2. Dimitris J. Bertsimas
3. Wei-Yin Loh
4. Robert Tibshirani
5. Nathan Kallus
6. Jerome H. Friedman
7. Alfred Kar Yin Truong
8. Jennifer L. Hill
9. R. Lalonde
10. Jaka Demsar

Who trusts Jack Dunn overall

1. Vinícius G. Costa
2. Emilio Carrizosa
3. Dimitris J. Bertsimas
4. Rafael Blanquero
5. Carlos Eduardo Pedreira
6. Cristina Molero-Río
7. Susan Athey
8. Dolores Romero Morales
9. Cynthia Rudin
10. Sina Aghaei

Papers from Jack Dunn's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Regression Shrinkage and Selection via the Lasso
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. The Nature of Statistical Learning Theory
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. Bagging Predictors
9. Attention is All you Need (Neural Information Processing Systems)
10. The Elements of Statistical Learning

Their papers

• Robust Classification (2019) — 107 citations
• Optimal Prescriptive Trees (2019) — 109 citations
• Optimal trees for prediction and prescription (2018) — 47 citations
• Optimal classification trees (2017) — 736 citations