Xiaohai Sun

Statistical Learning Theory · 2006–2009

A researcher in the Science corpus: 6 papers; trusts 30 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Statistical Learning Theory — 23 citations into it
• Artificial Intelligence — 17 citations into it; #10,511 of 35,345 by trust here, #19,439 by citations
• Probabilistic & Bayesian Methods — 17 citations into it; #335 of 1,525 by trust here, #294 by citations
• Statistics and Probability — 16 citations into it
• Causal Inference & Discovery — 12 citations into it; #141 of 364 by trust here, #200 by citations
• Signal Processing — 10 citations into it
• Control and Systems Engineering — 8 citations into it
• Optimization & Training — 6 citations into it

Whom Xiaohai Sun trusts overall

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

1. Kenji Fukumizu
2. Arthur Gretton
3. Bernhard Schölkopf
4. Thomas Schreiber
5. Alex Smola
6. Francis R. Bach
7. Shai Fine
8. Dominik Janzing
9. Grace Wahba
10. Ingo Steinwart

Who trusts Xiaohai Sun overall

1. Dominik Janzing
2. Bernhard Schölkopf
3. Jan Peters
4. Arthur Gretton
5. Kenji Fukumizu
6. Joris M. Mooij
7. Aaditya Ramdas
8. Kun Zhang
9. Bharath K. Sriperumbudur
10. Dino Sejdinovic

Papers from Xiaohai Sun's perspective

Ordered by how strongly the researchers Xiaohai Sun 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. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A kernel-based causal learning algorithm (2007) — 83 citations
• Causal reasoning by evaluating the complexity of conditional densities with kernel methods (2008) — 38 citations
• Assessing Nonlinear Granger Causality from Multivariate Time Series (2008) — 35 citations
• Distinguishing Cause and Effect via Second Order Exponential Models (2009) — 18 citations
• Kernel Measures of Conditional Dependence (2007) — 683 citations
• Causal Inference by Choosing Graphs with Most Plausible Markov Kernels (2006) — 72 citations