Hoang Minh Le

Reinforcement Learning · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 94 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 103 citations into it; #570 of 2,242 by trust here, #869 by citations
• Artificial Intelligence — 89 citations into it; #6,512 of 35,345 by trust here, #9,465 by citations
• Management Science and Operations Research — 33 citations into it; #2,960 of 11,946 by trust here, #2,472 by citations
• Statistics and Probability — 26 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it; #18,906 of 21,575 by trust here, #15,162 by citations
• Control and Systems Engineering — 12 citations into it
• Evaluation & Benchmarks — 11 citations into it; #2,025 of 3,147 by trust here, #2,671 by citations
• Optimization & Training — 11 citations into it; #1,153 of 2,601 by trust here, #1,661 by citations

Whom Hoang Minh Le trusts overall

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

1. Richard S. Sutton
2. R´emi Munos
3. Andrew Barto
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Doina Precup
7. Csaba Szepesvari
8. Lihong Li
9. Diederik P. Kingma
10. Hado van Hasselt

Who trusts Hoang Minh Le overall

1. Nathan Kallus
2. Masatoshi Uehara
3. Nan Jiang
4. Yuping Luo
5. T. Paine
6. Eric Zhan
7. Ofir Nachum
8. Audrey Huang
9. Scott Fujimoto
10. Michael R. Zhang

Papers from Hoang Minh Le's perspective

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

Their papers

• Batch Policy Learning under Constraints (2019) — 401 citations
• Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning (2019) — 180 citations
• Hierarchical Imitation and Reinforcement Learning (2018) — 221 citations
• Coordinated Multi-Agent Imitation Learning (2017) — 212 citations