Kyunghwan Son

Reinforcement Learning · 2007–2020

A researcher in the Science corpus: 7 papers; trusts 56 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 101 citations into it; #922 of 2,242 by trust here, #1,139 by citations
• Artificial Intelligence — 78 citations into it; #11,094 of 35,345 by trust here, #12,387 by citations
• Electrical and Electronic Engineering — 26 citations into it; #11,216 of 16,539 by trust here, #8,322 by citations
• Computer Networks and Communications — 23 citations into it
• Game & Decision Theory — 20 citations into it
• Computational Theory and Mathematics — 16 citations into it
• Management Science and Operations Research — 12 citations into it
• LLM Agents & Tool Use — 11 citations into it

Whom Kyunghwan Son trusts overall

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

1. Jakob N. Foerster
2. Tabish Rashid
3. Ming Tan
4. Mikayel Samvelyan
5. Gregory Farquhar
6. Frans Adriaan Oliehoek
7. Peter Stone
8. Richard S. Sutton
9. Carlos Guestrin
10. Daphne Koller

Who trusts Kyunghwan Son overall

1. Tonghan Wang
2. Chenghao Li
3. Jianhao Wang
4. Christian Schröder de Witt
5. Tabish Rashid
6. Bei Peng
7. Chao Yu
8. Wendelin Böhmer
9. Sai Qian Zhang
10. Shimon Whiteson

Papers from Kyunghwan Son's perspective

Ordered by how strongly the researchers Kyunghwan Son 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Reinforcement Learning: An Introduction
8. Human-level control through deep reinforcement learning
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning (2019) — 1,068 citations
• Learning to Schedule Communication in Multi-agent Reinforcement Learning (2019) — 257 citations
• Base Station Operation and User Association Mechanisms for Energy-Delay Tradeoffs in Green Cellular Networks (2011) — 501 citations
• Dynamic Base Station Switching-On/Off Strategies for Green Cellular Networks (2013) — 450 citations
• Dynamic association for load balancing and interference avoidance in multi-cell networks (2007) — 363 citations
• QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning (2020) — 30 citations
• QOPT: Optimistic Value Function Decentralization for Cooperative Multi-Agent Reinforcement Learning (2020)