Yonathan Efroni

Reinforcement Learning · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 60 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 43 citations into it
• Artificial Intelligence — 37 citations into it
• Management Science and Operations Research — 17 citations into it
• Control and Systems Engineering — 9 citations into it
• Economics and Econometrics — 7 citations into it
• Optimization & Training — 6 citations into it

Whom Yonathan Efroni trusts overall

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

1. Richard S. Sutton
2. Martin L. Puterman
3. Ronald J. Williams
4. David Silver
5. R´emi Munos
6. Bruno Scherrer
7. John Schulman
8. Andrew Barto
9. Eitan Altman
10. Sham M. Kakade

Who trusts Yonathan Efroni overall

1. Hado van Hasselt
2. Matteo Hessel
3. Youngmin Oh
4. Aldo Pacchiano

Papers from Yonathan Efroni's perspective

Ordered by how strongly the researchers Yonathan Efroni 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. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Reinforcement Learning with Trajectory Feedback (2020) — 49 citations
• Beyond the One Step Greedy Approach in Reinforcement Learning (2018) — 55 citations
• Action Robust Reinforcement Learning and Applications in Continuous Control (2019) — 304 citations
• Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning (2018) — 27 citations