Hengshuai Yao

Reinforcement Learning · 2009–2019

A researcher in the Science corpus: 7 papers; trusts 127 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 144 citations into it
• Artificial Intelligence — 109 citations into it; #24,139 of 35,345 by trust here, #32,362 by citations
• Management Science and Operations Research — 33 citations into it
• Computational Theory and Mathematics — 31 citations into it
• Electrical and Electronic Engineering — 15 citations into it
• Optimization & Training — 15 citations into it
• Control and Systems Engineering — 13 citations into it
• Computer Networks and Communications — 12 citations into it

Whom Hengshuai Yao trusts overall

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

1. Richard S. Sutton
2. Dimitri P. Bertsekas
3. Jon M. Kleinberg
4. Marc G. Bellemare
5. Andrew Barto
6. Sepandar D. Kamvar
7. B. Liu
8. Pavel Berkhin
9. Andrew Y. Ng
10. Satinder Singh

Who trusts Hengshuai Yao overall

1. Ruiyi Zhang
2. Anuj Mahajan
3. Junhyuk Oh
4. Tejas D. Kulkarni
5. Tom Schaul
6. Shangtong Zhang
7. Tianhe Yu
8. Bo Dai
9. Theja Tulabandhula
10. Yuxi Li

Papers from Hengshuai Yao's perspective

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

Their papers

• Reinforcement Ranking (2013) — 5 citations
• Provably Convergent Off-Policy Actor-Critic with Function Approximation (2019) — 8 citations
• QUOTA: The Quantile Option Architecture for Reinforcement Learning (2018) — 33 citations
• Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation (2019) — 60 citations
• Distributional Reinforcement Learning for Efficient Exploration (2019) — 107 citations
• Universal Option Models (2014) — 32 citations
• Multi-Step Dyna Planning for Policy Evaluation and Control (2009) — 23 citations