Christian Wirth

Reinforcement Learning · with Fürnkranz · 2013–2017

A researcher in the Science corpus: 3 papers; trusts 67 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 67 citations into it; #724 of 2,242 by trust here, #965 by citations
• Artificial Intelligence — 52 citations into it
• Control and Systems Engineering — 13 citations into it
• Computational Theory and Mathematics — 10 citations into it
• Management Science and Operations Research — 7 citations into it
• Optimization & Training — 7 citations into it
• Robotics & Control — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Christian Wirth trusts overall

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

1. Richard S. Sutton
2. Andrew Y. Ng
3. Andrew Barto
4. Thorsten Joachims
5. Jan Peters
6. Pieter Abbeel
7. Riad Akrour
8. W. B. Knox
9. Nikolaus Hansen
10. Justin A. Boyan

Who trusts Christian Wirth overall

1. Aldo Pacchiano
2. Yang Gao
3. Christian M. Meyer
4. Aadirupa Saha
5. Jan Leike
6. David Krueger
7. Ellen R. Novoseller
8. Iryna Gurevych
9. Jonathan Lee
10. Ruohan Zhang

Papers from Christian Wirth's perspective

Ordered by how strongly the researchers Christian Wirth 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Survey of Preference-Based Reinforcement Learning Methods (2017) — 475 citations
• Model-Free Preference-Based Reinforcement Learning (2016) — 112 citations
• Preference-Based Reinforcement Learning: A Preliminary Survey (2013) — 30 citations