Marie-José Huguet

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 16 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #30,835 of 35,345 by trust here, #28,481 by citations
• Safety, Robustness & Interpretability — 38 citations into it
• Safety Research — 20 citations into it
• AI Ethics, Fairness & Society — 18 citations into it
• Information Systems — 15 citations into it; #7,412 of 8,771 by trust here, #7,705 by citations
• Computational Theory and Mathematics — 13 citations into it
• Algorithms & Complexity — 12 citations into it; #469 of 516 by trust here, #452 by citations
• Statistical Learning Theory — 8 citations into it

Whom Marie-José Huguet trusts overall

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

1. J. Ross Quinlan
2. Alexandra Chouldechova
3. Toon Calders
4. Trevor J. Hastie
5. Alexey Ignatiev
6. Leo Breiman
7. Niko Speybroeck
8. Indrė Žliobaitė
9. Emir Demirovi'c
10. Julius Adebayo

Who trusts Marie-José Huguet overall

1. Carlos Eduardo Pedreira
2. Vinícius G. Costa

Papers from Marie-José Huguet's perspective

Ordered by how strongly the researchers Marie-José Huguet trusts endorse each paper. Same corpus, a different order for every researcher.

1. Fairness through awareness
2. Equality of Opportunity in Supervised Learning
3. ImageNet classification with deep convolutional neural networks
4. Random Forests
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Certifying and Removing Disparate Impact
7. Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
8. Fairness-aware Learning through Regularization Approach
9. C4.5: Programs for Machine Learning
10. Association for Computational Linguistics " Why Should I Trust You? " Explaining the Predictions of Any Classifier

Their papers

• Learning Optimal Decision Trees with MaxSAT and its Integration in AdaBoost (2020) — 54 citations
• Learning Fair Rule Lists (2019) — 11 citations