Yali Du

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 3 papers; trusts 52 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 142 citations into it
• Reinforcement Learning — 122 citations into it; #2,041 of 2,242 by trust here, #1,999 by citations
• Control and Systems Engineering — 45 citations into it
• Management Science and Operations Research — 41 citations into it
• Computational Theory and Mathematics — 29 citations into it
• Optimization & Training — 17 citations into it
• Statistics and Probability — 9 citations into it

Whom Yali Du trusts overall

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

1. Richard S. Sutton
2. John Schulman
3. Ronald J. Williams
4. Andrew Barto
5. Geoffrey E. Hinton
6. Diederik P. Kingma
7. Pieter Abbeel
8. Yoshua Bengio
9. Alex Krizhevsky
10. Sergey Levine

Who trusts Yali Du overall

1. Tabish Rashid
2. Jiechuan Jiang
3. Georgios Papoudakis
4. Bei Peng
5. Kyunghwan Son
6. Gregory Farquhar
7. Christian Schröder de Witt
8. Mikayel Samvelyan
9. Zongqing Lu
10. Filippos Christianos

Papers from Yali Du's perspective

Ordered by how strongly the researchers Yali Du 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Reinforcement Learning: An Introduction

Their papers

• A Review of Safe Reinforcement Learning: Methods, Theories, and Applications (2024) — 298 citations
• A Review of Safe Reinforcement Learning: Methods, Theory and Applications (2022) — 328 citations
• LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning (2019) — 194 citations