Pierre M'enard

Artificial Intelligence · 2020

A researcher in the Science corpus: 3 papers; trusts 22 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it
• Reinforcement Learning — 28 citations into it
• Management Science and Operations Research — 26 citations into it
• Statistical Learning Theory — 8 citations into it
• Electrical and Electronic Engineering — 7 citations into it

Whom Pierre M'enard trusts overall

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

1. Tor Lattimore
2. Mohammad Gheshlaghi Azar
3. Christoph Dann
4. Thomas Jaksch
5. Martin L. Puterman
6. Andrew Barto
7. Ian Osband
8. Emma Brunskill
9. Guanghua Hu
10. Richard S. Sutton

Papers from Pierre M'enard's perspective

Ordered by how strongly the researchers Pierre M'enard trusts endorse each paper. Same corpus, a different order for every researcher.

1. Reinforcement Learning: An Introduction
2. ImageNet classification with deep convolutional neural networks
3. Markov Decision Processes: Discrete Stochastic Dynamic Programming
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Near-optimal Regret Bounds for Reinforcement Learning
6. Gradient-based learning applied to document recognition
7. Near-Optimal Reinforcement Learning in Polynomial Time
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Human-level control through deep reinforcement learning
10. Asynchronous Methods for Deep Reinforcement Learning

Their papers

• A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces (2020) — 45 citations
• Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited (2020) — 118 citations
• Regret Bounds for Kernel-Based Reinforcement Learning (2020) — 22 citations