Sina Aghaei

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 36 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 30 citations into it
• Information Systems — 12 citations into it
• Safety, Robustness & Interpretability — 11 citations into it
• Algorithms & Complexity — 10 citations into it
• AI Ethics, Fairness & Society — 8 citations into it; #149 of 494 by trust here, #271 by citations
• Statistical Learning Theory — 8 citations into it; #602 of 1,038 by trust here, #678 by citations
• Safety Research — 8 citations into it; #1,386 of 5,829 by trust here, #2,686 by citations
• Mathematical Optimization — 6 citations into it

Whom Sina Aghaei trusts overall

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

1. J. Ross Quinlan
2. Faisal Kamiran
3. Dimitris J. Bertsimas
4. Sicco Verwer
5. Toon Calders
6. Leo Breiman
7. Niko Speybroeck
8. Velibor V. Misic
9. Ron Kohavi
10. Siegfried Nijssen

Who trusts Sina Aghaei overall

1. Emir Demirovi'c
2. Haoran Zhu
3. Adam N. Elmachtoub
4. Emilio Carrizosa
5. Vinícius G. Costa
6. Valentina Zantedeschi
7. Ali Aouad
8. Pavankumar Murali
9. Anna Lukina
10. Cristina Molero-Río

Papers from Sina Aghaei's perspective

Ordered by how strongly the researchers Sina Aghaei 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Induction of decision trees
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Attention is All you Need (Neural Information Processing Systems)
10. C4.5: Programs for Machine Learning

Their papers

• Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making (2019) — 192 citations
• Learning Optimal Classification Trees: Strong Max-Flow Formulations (2020) — 28 citations