Michail G. Lagoudakis

Reinforcement Learning · 2001–2017

A researcher in the Science corpus: 9 papers; trusts 26 researchers and is trusted by 381 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 37 citations into it; #66 of 2,242 by trust here, #246 by citations
• Artificial Intelligence — 12 citations into it; #13,777 of 35,345 by trust here, #19,018 by citations
• Computer Networks and Communications — 5 citations into it; #3,049 of 9,886 by trust here, #5,518 by citations

Whom Michail G. Lagoudakis trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Andrew Y. Ng
4. Justin A. Boyan
5. Steven J. Bradtke
6. Ben J. A. Kröse
7. Geoffrey J. Gordon
8. Longxin Lin
9. Sham M. Kakade
10. Ronan Collobert

Who trusts Michail G. Lagoudakis overall

1. Bruno Scherrer
2. Amir-massoud Farahmand
3. Mohammad Ghavamzadeh
4. Csaba Szepesvari
5. Alessandro Lazaric
6. Sergey Levine
7. Richard S. Sutton
8. J. Urgen Schmidhuber
9. Matthieu Geist
10. J. Kolter

Papers from Michail G. Lagoudakis's perspective

Ordered by how strongly the researchers Michail G. Lagoudakis 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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

• Learning from Labeled and Unlabeled Data (2010) — 355 citations
• Learning to Select Branching Rules in the DPLL Procedure for Satisfiability (2001) — 105 citations
• Auction-Based Multi-Robot Routing (2005) — 336 citations
• Encyclopedia of Machine Learning and Data Mining (2017) — 452 citations
• Learning in Logic (2010) — 104 citations
• Least-Squares Policy Iteration (2003) — 1,500 citations
• Reinforcement Learning as Classification: Leveraging Modern Classifiers (2003) — 184 citations
• Value Function Approximation in Zero-Sum Markov Games (2002) — 81 citations
• Coordinated Reinforcement Learning (2002) — 450 citations