Matthieu Komorowski

Reinforcement Learning · with Faisal · 2017–2020

A researcher in the Science corpus: 11 papers; trusts 46 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 75 citations into it; #787 of 2,242 by trust here, #591 by citations
• Artificial Intelligence — 69 citations into it; #12,546 of 35,345 by trust here, #6,614 by citations
• Epidemiology — 48 citations into it; #540 of 2,148 by trust here, #772 by citations
• Statistics and Probability — 35 citations into it; #12,091 of 12,932 by trust here, #6,310 by citations
• Management Science and Operations Research — 24 citations into it
• Medical & Health AI — 22 citations into it; #55 of 239 by trust here, #36 by citations
• Evaluation & Benchmarks — 18 citations into it
• Surgery — 16 citations into it

Whom Matthieu Komorowski trusts overall

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

1. Richard S. Sutton
2. Nan Jiang
3. Ronald J. Williams
4. Yoshua Bengio
5. Sergey Ioffe
6. P. Marik
7. Lihong Li
8. Diederik P. Kingma
9. Mohit Sewak
10. Andrew Barto

Who trusts Matthieu Komorowski overall

1. Chao Yu
2. Joseph D. Futoma
3. Jiming Liu
4. Chaochao Lu
5. Michael Oberst
6. Michael C. Hughes
7. M. El-Haddad,
8. Omer Gottesman
9. Aniruddh Raghu
10. Finale Doshi-Velez

Papers from Matthieu Komorowski's perspective

Ordered by how strongly the researchers Matthieu Komorowski 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. Reinforcement Learning: An Introduction
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Evaluating Reinforcement Learning Algorithms in Observational Health Settings (2018) — 151 citations
• Representation Balancing MDPs for Off-Policy Policy Evaluation (2018) — 75 citations
• Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach (2017) — 238 citations
• Guidelines for reinforcement learning in healthcare (2019) — 449 citations
• Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies (2020) — 944 citations
• The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Making (2018) — 20 citations
• Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters (2018) — 37 citations
• The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care (2018) — 1,233 citations
• Model-Based Reinforcement Learning for Sepsis Treatment (2018) — 60 citations
• Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning (2018) — 110 citations
• Deep Reinforcement Learning for Sepsis Treatment (2017) — 200 citations