Chu Wang

Optimization & Training · with Schapire · 2015

A researcher in the Science corpus: 1 paper; trusts 6 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 12 citations into it
• Artificial Intelligence — 8 citations into it; #29,774 of 35,345 by trust here, #31,361 by citations
• Statistics and Probability — 6 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Chu Wang trusts overall

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

1. Jerome H. Friedman
2. Leo Breiman
3. Martin Jaggi
4. Robert E. Schapire
5. Vladimir Koltchinskii
6. Robert Tibshirani
7. Llew Mason
8. Yoav Freund
9. Tong Zhang
10. Philip Wolfe

Who trusts Chu Wang overall

1. Evgenii Egorov
2. Valentina Zantedeschi
3. Anna Kuzina
4. Francesco Locatello
5. Evgeny Burnaev
6. Aurélien Bellet
7. Kirill Neklyudov
8. Rajiv Khanna

Papers from Chu Wang's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. Greedy function approximation: A gradient boosting machine.
3. ImageNet classification with deep convolutional neural networks
4. Special Invited Paper-Additive logistic regression: A statistical view of boosting
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Gradient-based learning applied to document recognition
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. A decision-theoretic generalization of on-line learning and an application to boosting
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. The Nature of Statistical Learning Theory

Their papers

• Functional Frank-Wolfe Boosting for General Loss Functions (2015) — 21 citations