Yun Yang

Statistics and Probability · with Bhattacharya · 2015–2017

A researcher in the Science corpus: 6 papers; trusts 95 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 90 citations into it; #3,410 of 12,932 by trust here, #4,132 by citations
• Artificial Intelligence — 82 citations into it; #8,579 of 35,345 by trust here, #15,542 by citations
• Computational Mechanics — 30 citations into it; #5,970 of 10,001 by trust here, #8,566 by citations
• Statistical Learning Theory — 29 citations into it
• Optimization & Training — 16 citations into it
• Computer Networks and Communications — 15 citations into it
• Management Science and Operations Research — 14 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it

Whom Yun Yang trusts overall

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

1. Victor Chernozhukov
2. Peter L. Bartlett
3. Grace Wahba
4. Joel A. Tropp
5. A. van der Vaart
6. Francis R. Bach
7. Michel Ledoux
8. Michael W. Mahoney
9. Jerome H. Friedman
10. David Ruppert

Who trusts Yun Yang overall

1. Lin F. Yang
2. Luigi Carratino
3. Jonathan Brophy
4. Ahmed M. Alaa
5. Mengdi Wang
6. Pierre Alquier
7. Arsalan Sharifnassab
8. Saber Salehkaleybar
9. Alessandro Rudi
10. Daniel Lowd

Papers from Yun Yang's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Regression Shrinkage and Selection via the Lasso
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Randomized sketches for kernels: Fast and optimal non-parametric regression (2015) — 188 citations
• Communication-efficient distributed statistical learning (2016) — 19 citations
• Bayesian regression tree ensembles that adapt to smoothness and sparsity (2017) — 187 citations
• Communication-Efficient Distributed Statistical Inference (2016) — 520 citations
• Bayesian fractional posteriors (2016) — 133 citations
• Frequentist coverage and sup-norm convergence rate in Gaussian process regression (2017) — 59 citations