Maria Dimakopoulou

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 54 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #17,328 of 35,345 by trust here, #22,463 by citations
• Management Science and Operations Research — 63 citations into it; #5,116 of 11,946 by trust here, #5,787 by citations
• Reinforcement Learning — 49 citations into it; #1,612 of 2,242 by trust here, #1,973 by citations
• Statistics and Probability — 31 citations into it
• Statistical Learning Theory — 15 citations into it
• Causal Inference & Discovery — 13 citations into it
• Computer Networks and Communications — 6 citations into it
• Electrical and Electronic Engineering — 6 citations into it; #8,782 of 16,539 by trust here, #13,165 by citations

Whom Maria Dimakopoulou trusts overall

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

1. Lihong Li
2. Susan Athey
3. Nathan Kallus
4. T. L. Lai Andherbertrobbins
5. Bianca Zadrozny
6. Steven L. Scott
7. J. Langford
8. Peter Auer
9. Robert Tibshirani
10. Alekh Agarwal

Who trusts Maria Dimakopoulou overall

1. Susan Athey
2. Tonghan Wang
3. Yash Deshpande
4. Zhengyuan Zhou
5. Jianhao Wang
6. Guido W. Imbens
7. Lester W. Mackey
8. Chao Yu
9. Yi Wu

Papers from Maria Dimakopoulou's perspective

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

Their papers

• Coordinated Exploration in Concurrent Reinforcement Learning (2018) — 46 citations
• Estimation Considerations in Contextual Bandits (2017) — 75 citations
• Balanced Linear Contextual Bandits (2018) — 77 citations