Thomas B. Berrett

Statistics and Probability · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 51 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 70 citations into it; #3,710 of 12,932 by trust here, #4,964 by citations
• Artificial Intelligence — 51 citations into it
• Finance — 15 citations into it
• Statistical Learning Theory — 13 citations into it
• Causal Inference & Discovery — 12 citations into it
• Economics and Econometrics — 9 citations into it; #8,765 of 23,481 by trust here, #17,346 by citations
• Probabilistic & Bayesian Methods — 9 citations into it
• Computational Theory and Mathematics — 8 citations into it

Whom Thomas B. Berrett trusts overall

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

1. Wicher P. Bergsma
2. Arthur Gretton
3. Bernhard Schölkopf
4. Michael I. Jordan
5. Kun Zhang
6. Kenji Fukumizu
7. E.J. Candes
8. G'abor J. Sz'ekely
9. Liangjun Su
10. Francis R. Bach

Who trusts Thomas B. Berrett overall

1. Hongjian Shi
2. Nabarun Deb
3. Mathias Drton
4. Fang Han
5. Weihao Gao
6. Zhexiao Lin
7. Bodhisattva Sen
8. Yi Wang
9. Sewoong Oh
10. Rajen Dinesh Shah

Papers from Thomas B. Berrett's perspective

Ordered by how strongly the researchers Thomas B. Berrett 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. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Nonparametric independence testing via mutual information (2017) — 119 citations
• Efficient multivariate entropy estimation via $k$-nearest neighbour distances (2016) — 140 citations
• The conditional permutation test for independence while controlling for confounders (2018) — 153 citations
• Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms (2020) — 40 citations
• The conditional permutation test (2018) — 16 citations