Hamsa Bastani

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 61 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it; #5,647 of 35,345 by trust here, #10,566 by citations
• Management Science and Operations Research — 34 citations into it; #2,611 of 11,946 by trust here, #6,888 by citations
• Safety, Robustness & Interpretability — 25 citations into it; #557 of 2,251 by trust here, #1,078 by citations
• Statistics and Probability — 22 citations into it
• Reinforcement Learning — 19 citations into it
• Statistical Learning Theory — 15 citations into it
• Computer Networks and Communications — 10 citations into it
• Medical & Health AI — 10 citations into it

Whom Hamsa Bastani trusts overall

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

1. Alexandre B. Tsybakov
2. Peter Auer
3. Eric R. Ziegel
4. Aleksandrs Slivkins
5. Varsha Dani
6. Yasin Abbasi-Yadkori
7. Daniel Russo
8. Assaf J. Zeevi
9. Sham M. Kakade
10. Alexander Goldenshluger

Who trusts Hamsa Bastani overall

1. Susan Athey
2. Nathan Kallus
3. Maria Dimakopoulou
4. Carolyn Kim
5. Guido W. Imbens
6. Osbert Bastani
7. Alberto Bietti
8. Jack Dunn
9. Zhengyuan Zhou
10. Alexandra Chouldechova

Papers from Hamsa Bastani's perspective

Ordered by how strongly the researchers Hamsa Bastani 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. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Online Decision Making with High-Dimensional Covariates (2020) — 433 citations
• Interpreting Blackbox Models via Model Extraction (2017) — 196 citations
• Interpretability via Model Extraction (2017) — 139 citations
• Exploiting the Natural Exploration In Contextual Bandits (2017) — 30 citations