Sean P. Meyn

Statistics and Probability · 1993–2017

A researcher in the Science corpus: 8 papers; trusts 54 researchers and is trusted by 234 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 30 citations into it; #3,077 of 12,932 by trust here, #3,513 by citations
• Reinforcement Learning — 22 citations into it; #481 of 2,242 by trust here, #1,227 by citations
• Artificial Intelligence — 19 citations into it; #4,284 of 35,345 by trust here, #9,870 by citations
• Management Information Systems — 15 citations into it; #181 of 3,376 by trust here, #461 by citations
• Computer Networks and Communications — 14 citations into it; #1,636 of 9,886 by trust here, #5,961 by citations
• Mathematical Physics — 14 citations into it; #3,045 of 8,562 by trust here, #3,022 by citations
• Finance — 11 citations into it; #897 of 10,518 by trust here, #1,800 by citations
• Mathematical Optimization — 10 citations into it

Whom Sean P. Meyn trusts overall

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

1. Atilla Eryilmaz
2. Alexander L. Stolyar
3. T. Lindvall
4. Vivek Shripad Borkar
5. Dimitri P. Bertsekas
6. Rayadurgam Srikant
7. Suresh P. Sethi
8. Csaba Szepesvari
9. Ishai Menache
10. Richard S. Sutton

Who trusts Sean P. Meyn overall

1. Martin Hairer
2. Gareth O. Roberts
3. Huizhen Yu
4. Arnaud Guillin
5. Jeffrey S. Rosenthal
6. Andrew M. Stuart
7. Peter W. Glynn
8. Ioannis Kontoyiannis
9. Òscar Celma
10. Shie Mannor

Papers from Sean P. Meyn's perspective

Ordered by how strongly the researchers Sean P. Meyn 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. Reinforcement Learning: An Introduction
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• An analysis of reinforcement learning with function approximation (2008) — 288 citations
• The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning (2000) — 451 citations
• Spectral theory and limit theorems for geometrically ergodic Markov processes (2002) — 276 citations
• Large Deviations Asymptotics and the Spectral Theory of Multiplicatively Regular Markov Processes (2005) — 151 citations
• Control Techniques for Complex Networks: Workload (2007) — 365 citations
• Geometric ergodicity and the spectral gap of non-reversible Markov chains (2009) — 113 citations
• Zap Q-Learning (2017) — 100 citations
• Markov Chains and Stochastic Stability (1993)