Adam N. Elmachtoub

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 29 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 28 citations into it; #15,128 of 35,345 by trust here, #25,809 by citations
• Statistics and Probability — 22 citations into it
• Management Science and Operations Research — 17 citations into it; #4,851 of 11,946 by trust here, #8,062 by citations
• Optimization & Training — 16 citations into it
• Statistical Learning Theory — 12 citations into it
• Algorithms & Complexity — 5 citations into it
• Information Systems — 5 citations into it

Whom Adam N. Elmachtoub trusts overall

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

1. L. Eon Bottou
2. Dimitris J. Bertsimas
3. Priya Lekha Donti
4. Shai Ben-David
5. Nathan Kallus
6. Niko Speybroeck
7. Sina Aghaei
8. Dimitri P. Bertsekas
9. Iain Dunning
10. Ravindra K. Ahuja

Who trusts Adam N. Elmachtoub overall

1. Ali Aouad
2. Xinkun Nie
3. P. MarinVlastelica
4. Ryan McNellis
5. Kris Johnson Ferreira
6. Emma Brunskill
7. Jason Cheuk Nam Liang
8. Anselm Paulus
9. François Soumis
10. Stefan Wager

Papers from Adam N. Elmachtoub's perspective

Ordered by how strongly the researchers Adam N. Elmachtoub 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Smart "Predict, then Optimize" (2017) — 889 citations
• Market Segmentation Trees (2019) — 29 citations
• Decision Trees for Decision-Making under the Predict-then-Optimize Framework (2020) — 147 citations