Rajen Dinesh Shah

Statistics and Probability · 2011–2018

A researcher in the Science corpus: 4 papers; trusts 99 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 79 citations into it; #2,275 of 12,932 by trust here, #2,858 by citations
• Artificial Intelligence — 43 citations into it; #9,603 of 35,345 by trust here, #19,018 by citations
• Statistical Learning Theory — 19 citations into it
• Computational Mechanics — 12 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it; #1,448 of 1,525 by trust here, #1,075 by citations
• Optimization & Training — 8 citations into it
• Molecular Biology — 7 citations into it

Whom Rajen Dinesh Shah trusts overall

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

1. Robert Tibshirani
2. Cun-Hui Zhang
3. Jerome H. Friedman
4. Nicolai Meinshausen
5. Trevor J. Hastie
6. Victor Chernozhukov
7. Leo Breiman
8. Peter Buhlmann
9. Peter Bhlmann
10. Sara A. van de Geer

Who trusts Rajen Dinesh Shah overall

1. Jan Peters
2. Sonja Greven
3. Xiangyu Wang
4. Lorentz Jäntschi
5. Christina Heinze-Deml
6. Bernhard Schölkopf
7. Peter Buhlmann
8. Tianqi Zhao
9. Nicolai Meinshausen
10. Wenhao Hu

Papers from Rajen Dinesh Shah's perspective

Ordered by how strongly the researchers Rajen Dinesh Shah 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Regression Shrinkage and Selection via the Lasso
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Variable selection with error control: another look at stability selection (2011) — 406 citations
• Goodness‐of‐fit tests for high dimensional linear models (2015) — 54 citations
• The hardness of conditional independence testing and the generalised covariance measure (2018) — 381 citations
• Random intersection trees (2013) — 48 citations