Xinkun Nie

Statistics and Probability · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 122 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 135 citations into it; #3,184 of 12,932 by trust here, #4,809 by citations
• Artificial Intelligence — 59 citations into it
• Causal Inference & Discovery — 43 citations into it; #118 of 364 by trust here, #200 by citations
• Reinforcement Learning — 37 citations into it
• Statistical Learning Theory — 35 citations into it
• Management Science and Operations Research — 31 citations into it
• Economics and Econometrics — 16 citations into it
• Optimization & Training — 14 citations into it

Whom Xinkun Nie trusts overall

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

1. James M. Robins
2. Susan Athey
3. Donald B. Rubin
4. Robert Tibshirani
5. Susan A. Murphy
6. Stefan Wager
7. Peter M. Robinson
8. Whitney K. Newey
9. Anastasios A. Tsiatis
10. Victor Chernozhukov

Who trusts Xinkun Nie overall

1. Susan Athey
2. Edward H. Kennedy
3. Stefan Wager
4. Liangyuan Hu
5. Rina Friedberg
6. David A. Hirshberg
7. Nathan Kallus
8. Nikolaos Ignatiadis
9. Julie Tibshirani
10. Liuyi Yao

Papers from Xinkun Nie's perspective

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

Their papers

• Quasi-oracle estimation of heterogeneous treatment effects (2017) — 871 citations
• Why adaptively collected data have negative bias and how to correct for it (2017) — 97 citations
• Learning When-to-Treat Policies (2019) — 100 citations
• Learning Objectives for Treatment Effect Estimation (2017) — 67 citations