Kee-Eung Kim

Reinforcement Learning · 1999–2022

A researcher in the Science corpus: 9 papers; trusts 213 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 93 citations into it; #2,139 of 2,242 by trust here, #2,036 by citations
• Artificial Intelligence — 75 citations into it; #9,588 of 35,345 by trust here, #13,544 by citations
• Management Science and Operations Research — 21 citations into it; #4,216 of 11,946 by trust here, #3,357 by citations
• Computational Theory and Mathematics — 17 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it; #10,941 of 21,575 by trust here, #18,266 by citations
• Control and Systems Engineering — 14 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Statistics and Probability — 12 citations into it

Whom Kee-Eung Kim trusts overall

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

1. Pieter Abbeel
2. Richard S. Sutton
3. E. Todorov
4. Geoffrey E. Hinton
5. Andrew Y. Ng
6. Diederik P. Kingma
7. Christopher K. I. Williams
8. Michael I. Jordan
9. Sergey Levine
10. Leemon C. Baird III

Who trusts Kee-Eung Kim overall

1. Kun Li
2. J. Urgen Schmidhuber
3. Ilya Kostrikov
4. Sergey Levine
5. Minghuan Liu
6. Daan Wierstra
7. Saurabh Arora
8. Jonathan Baxter
9. Frans Adriaan Oliehoek
10. Joel W. Burdick

Papers from Kee-Eung Kim's perspective

Ordered by how strongly the researchers Kee-Eung Kim 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. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning to Cooperate via Policy Search (2000) — 312 citations
• Imitation Learning via Kernel Mean Embedding (2018) — 44 citations
• Information-Theoretic Bounded Rationality (2015) — 59 citations
• GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems (2022) — 46 citations
• Representation Balancing Offline Model-based Reinforcement Learning (2021) — 52 citations
• A Bayesian Approach to Generative Adversarial Imitation Learning (2018) — 41 citations
• Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions (2012) — 139 citations
• Off-Policy Policy Search (2007) — 18 citations
• Learning Finite-State Controllers for Partially Observable Environments (1999)