Ruoxuan Xiong

Economics and Econometrics · 2015–2019

A researcher in the Science corpus: 4 papers; trusts 49 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Economics and Econometrics — 53 citations into it; #7,705 of 23,481 by trust here, #14,505 by citations
• Statistics and Probability — 39 citations into it
• Finance — 31 citations into it; #10,213 of 10,518 by trust here, #9,725 by citations
• Artificial Intelligence — 20 citations into it
• Causal Inference & Discovery — 12 citations into it
• Management Science and Operations Research — 11 citations into it; #3,763 of 11,946 by trust here, #6,888 by citations
• Optimization & Training — 7 citations into it
• Accounting — 5 citations into it

Whom Ruoxuan Xiong trusts overall

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

1. Jushan Bai
2. James H. Stock
3. Markus Pelger
4. Jinyong Hahn
5. Martin Lettau
6. Serena Ng
7. Mark W. Watson
8. Sahand N. Negahban
9. A. Onatski
10. Guido W. Imbens

Who trusts Ruoxuan Xiong overall

1. Avraam Tsantekidis
2. A. Navon
3. Omer Berat Sezer
4. Hamed Ghoddusi
5. Nikolaos Passalis
6. Yosi Keller
7. M. Ugur Gudelek
8. Anastasios Tefas
9. Germán G. Creamer
10. Xiong Wen Pang

Papers from Ruoxuan Xiong's perspective

Ordered by how strongly the researchers Ruoxuan Xiong 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Stable Prediction across Unknown Environments (2018) — 191 citations
• Deep Learning Stock Volatility with Google Domestic Trends (2015) — 97 citations
• Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference (2019) — 53 citations
• State-Varying Factor Models of Large Dimensions (2018) — 51 citations