Christoph Dann

Reinforcement Learning · 2015–2018

A researcher in the Science corpus: 9 papers; trusts 122 researchers and is trusted by 86 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 101 citations into it; #263 of 2,242 by trust here, #612 by citations
• Artificial Intelligence — 98 citations into it; #2,508 of 35,345 by trust here, #5,600 by citations
• Management Science and Operations Research — 49 citations into it; #620 of 11,946 by trust here, #1,243 by citations
• Statistical Learning Theory — 28 citations into it
• Computational Theory and Mathematics — 25 citations into it; #5,221 of 12,182 by trust here, #6,798 by citations
• Statistics and Probability — 23 citations into it
• Optimization & Training — 17 citations into it
• Control and Systems Engineering — 16 citations into it

Whom Christoph Dann trusts overall

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

1. Richard S. Sutton
2. Dimitri P. Bertsekas
3. Alexander L. Strehl
4. Lihong Li
5. Csaba Szepesvari
6. Sham M. Kakade
7. Andrew Barto
8. Andrew Y. Ng
9. Michael L. Littman
10. Geoffrey E. Hinton

Who trusts Christoph Dann overall

1. Nan Jiang
2. Ian Osband
3. Brendan O'Donoghue
4. A. Mahmood
5. Simon Shaolei Du
6. Adam White
7. Huizhen Yu
8. Richard S. Sutton
9. B. Recht
10. Ben M. Hambly

Papers from Christoph Dann's perspective

Ordered by how strongly the researchers Christoph Dann 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. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Policy evaluation with temporal differences: a survey and comparison (2015) — 252 citations
• Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning (2017) — 329 citations
• Thoughts on Massively Scalable Gaussian Processes (2015) — 116 citations
• Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning (2015) — 266 citations
• On Oracle-Efficient PAC RL with Rich Observations (2018) — 107 citations
• Sample Efficient Policy Search for Optimal Stopping Domains (2017) — 9 citations
• On Polynomial Time PAC Reinforcement Learning with Rich Observations (2018) — 9 citations
• The Human Kernel (2015) — 69 citations
• RLPy: a value-function-based reinforcement learning framework for education and research (2015) — 77 citations