Eirini Ntoutsi

Artificial Intelligence · 2006–2021

A researcher in the Science corpus: 7 papers; trusts 60 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 121 citations into it; #13,159 of 35,345 by trust here, #13,353 by citations
• AI Ethics, Fairness & Society — 66 citations into it
• Safety Research — 62 citations into it; #2,250 of 5,829 by trust here, #2,873 by citations
• Safety, Robustness & Interpretability — 53 citations into it
• Sociology and Political Science — 33 citations into it
• Information Systems — 24 citations into it
• Statistics and Probability — 23 citations into it
• Signal Processing — 11 citations into it; #5,368 of 7,572 by trust here, #5,340 by citations

Whom Eirini Ntoutsi trusts overall

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

1. Faisal Kamiran
2. Toon Calders
3. Albert Bifet
4. Indrė Žliobaitė
5. Charu C. Aggarwal
6. Latanya Sweeney
7. Robert E. Schapire
8. Martin Ester
9. Dino Pedreschi
10. Pedro M. Domingos

Who trusts Eirini Ntoutsi overall

1. Vasileios Iosifidis
2. Charu C. Aggarwal
3. Steven C. H. Hoi
4. Marwan Hassani
5. Yuji Roh
6. Simon Caton
7. Lin Yue
8. Ulrich Matchi Aïvodji
9. Yu-Ru Lin
10. Hai-Long Nguyen

Papers from Eirini Ntoutsi's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• A survey on datasets for fairness‐aware machine learning (2021) — 354 citations
• Bias in data‐driven artificial intelligence systems—An introductory survey (2020) — 903 citations
• FAHT: An Adaptive Fairness-aware Decision Tree Classifier (2019) — 121 citations
• AdaFair: Cumulative Fairness Adaptive Boosting (2019) — 97 citations
• Large Scale Sentiment Learning with Limited Labels (2017) — 34 citations
• Density-based Projected Clustering over High Dimensional Data Streams (2012) — 117 citations
• MONIC: modeling and monitoring cluster transitions (2006) — 314 citations