Yifei Ma

Reinforcement Learning · with Wang · 2019

A researcher in the Science corpus: 2 papers; trusts 41 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 36 citations into it; #836 of 2,242 by trust here, #1,663 by citations
• Artificial Intelligence — 23 citations into it; #10,103 of 35,345 by trust here, #19,873 by citations
• Management Science and Operations Research — 20 citations into it
• Statistics and Probability — 18 citations into it; #3,565 of 12,932 by trust here, #5,126 by citations
• Causal Inference & Discovery — 5 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Yifei Ma trusts overall

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

1. James M. Robins
2. Richard S. Sutton
3. Lihong Li
4. Philip S. Thomas
5. Miroslav Dud'ik
6. Nan Jiang
7. J. Langford
8. Satinder Singh
9. Sham M. Kakade
10. Yu-Xiang Wang

Who trusts Yifei Ma overall

1. Nathan Kallus
2. Masatoshi Uehara
3. Nan Jiang
4. Andrea Zanette
5. Zhuangdi Zhu
6. Martin Klissarov
7. Jiawei Huang
8. Ming Yin
9. Cameron Voloshin
10. Ali Mousavi

Papers from Yifei Ma's perspective

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

Their papers

• Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling (2019) — 199 citations
• Imitation-Regularized Offline Learning (2019) — 28 citations