Pierre Kreitmann

Artificial Intelligence · 2011–2019

A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #21,814 of 35,345 by trust here, #26,682 by citations
• Reinforcement Learning — 17 citations into it
• Management Science and Operations Research — 8 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it

Whom Pierre Kreitmann trusts overall

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

1. Richard S. Sutton
2. Yaakov Engel
3. Malcolm J. A. Strens
4. Andrew Barto
5. Michael Kearns
6. Michael L. Littman

Who trusts Pierre Kreitmann overall

1. R´emi Munos
2. Chris Amato

Papers from Pierre Kreitmann's perspective

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

1. Reinforcement Learning: An Introduction
2. R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
3. Near-Optimal Reinforcement Learning in Polynomial Time
4. ImageNet classification with deep convolutional neural networks
5. Planning and Acting in Partially Observable Stochastic Domains
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Gradient-based learning applied to document recognition
8. Introduction to Reinforcement Learning
9. Reinforcement learning with selective perception and hidden state
10. A Bayesian Framework for Reinforcement Learning

Their papers

• A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes (2011) — 155 citations
• Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements (2019) — 176 citations