Bertrand Michel

Computational Theory and Mathematics · 2013–2017

A researcher in the Science corpus: 5 papers; trusts 66 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 35 citations into it; #3,554 of 12,182 by trust here, #5,465 by citations
• Artificial Intelligence — 22 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it; #8,800 of 21,575 by trust here, #12,689 by citations
• Statistics and Probability — 19 citations into it; #5,725 of 12,932 by trust here, #7,914 by citations
• Molecular Biology — 15 citations into it; #5,107 of 17,773 by trust here, #12,008 by citations
• Mathematical Physics — 12 citations into it; #8,040 of 8,562 by trust here, #7,868 by citations
• Statistical Learning Theory — 9 citations into it
• Applied Mathematics — 8 citations into it

Whom Bertrand Michel trusts overall

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

1. Leo Breiman
2. Trevor J. Hastie
3. Robert Tibshirani
4. Antonio Cuevas
5. David L. Donoho
6. Isabelle M Guyon
7. Steve Y. Oudot
8. Marco Sandri
9. Peter Bubenik
10. Eric R. Ziegel

Who trusts Bertrand Michel overall

1. Aaron Fisher
2. Cynthia Rudin
3. Francesca Dominici
4. Frédéric Chazal
5. Giles Hooker
6. Tomasz Górecki
7. Christian Bick
8. Robin Genuer
9. Baptiste Gregorutti
10. Zhongzhan Huang

Papers from Bertrand Michel's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. Bagging Predictors
3. Random Forests
4. The Elements of Statistical Learning
5. Stability of Persistence Diagrams (Discrete & Computational Geometry)
6. Model selection and estimation in regression with grouped variables
7. ImageNet classification with deep convolutional neural networks
8. The Nature of Statistical Learning Theory
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Estimating the reach of a manifold (2017) — 123 citations
• Grouped variable importance with random forests and application to multiple functional data analysis (2014) — 140 citations
• Correlation and variable importance in random forests (2013) — 996 citations
• An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists (2017) — 820 citations
• Statistical Analysis and Parameter Selection for Mapper (2017) — 113 citations