Yaodong Yang

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 16 papers; trusts 175 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 340 citations into it; #12,021 of 35,345 by trust here, #5,533 by citations
• Reinforcement Learning — 301 citations into it; #1,217 of 2,242 by trust here, #574 by citations
• Computer Vision and Pattern Recognition — 69 citations into it
• Management Science and Operations Research — 65 citations into it; #10,833 of 11,946 by trust here, #2,310 by citations
• Game & Decision Theory — 50 citations into it
• Control and Systems Engineering — 42 citations into it
• Computational Theory and Mathematics — 41 citations into it
• Natural Language Processing — 41 citations into it

Whom Yaodong Yang trusts overall

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

1. Michael L. Littman
2. Richard S. Sutton
3. Geoffrey E. Hinton
4. Junling Hu
5. Jakob N. Foerster
6. Volodymyr Mnih
7. Alex Graves
8. Yoshua Bengio
9. Diederik P. Kingma
10. Alex Krizhevsky

Who trusts Yaodong Yang overall

1. Jiechuan Jiang
2. Tabish Rashid
3. Jianhao Wang
4. Sven Gronauer
5. Kyunghwan Son
6. Kaiqing Zhang
7. Filippos Christianos
8. Tonghan Wang
9. Xiaoteng Ma
10. Gregory Farquhar

Papers from Yaodong Yang's perspective

Ordered by how strongly the researchers Yaodong Yang 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games (2017) — 373 citations
• Multi-Agent Reinforcement Learning is a Sequence Modeling Problem (2022) — 362 citations
• An Empirical Study of AI Population Dynamics with Million-agent Reinforcement Learning (2017) — 7 citations
• Mean Field Multi-Agent Reinforcement Learning (2018) — 717 citations
• Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning (2019) — 169 citations
• Factorized Q-learning for large-scale multi-agent systems (2018) — 75 citations
• Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning (2021) — 407 citations
• A Study of AI Population Dynamics with Million-agent Reinforcement Learning (2017) — 41 citations
• Safe RLHF: Safe Reinforcement Learning from Human Feedback (2023) — 750 citations
• BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset (2023) — 964 citations
• AI Alignment: A Comprehensive Survey (2023) — 376 citations
• A Review of Safe Reinforcement Learning: Methods, Theory and Applications (2022) — 328 citations
• Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games (2021) — 20 citations
• Q-value Path Decomposition for Deep Multiagent Reinforcement Learning (2020) — 65 citations
• Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning (2020) — 236 citations
• Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games (2017) — 216 citations