Gautam Kamath

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 16 papers; trusts 108 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 394 citations into it; #3,762 of 35,345 by trust here, #3,289 by citations
• Privacy & Security — 178 citations into it
• Statistics and Probability — 132 citations into it; #2,450 of 12,932 by trust here, #867 by citations
• Computational Theory and Mathematics — 63 citations into it; #3,989 of 12,182 by trust here, #3,924 by citations
• Safety, Robustness & Interpretability — 53 citations into it; #1,559 of 2,251 by trust here, #1,349 by citations
• Computer Science Applications — 48 citations into it
• Statistical Learning Theory — 46 citations into it; #453 of 1,038 by trust here, #243 by citations
• Electrical and Electronic Engineering — 29 citations into it

Whom Gautam Kamath trusts overall

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

1. Cynthia Dwork
2. Mark Bun
3. Kobbi Nissim
4. Frank McSherry
5. Adam D. Smith
6. Ilias Diakonikolas
7. Kunal Talwar
8. Thomas Steinke
9. Jonathan Ullman
10. Moritz Hardt

Who trusts Gautam Kamath overall

1. Ilias Diakonikolas
2. Jayadev Acharya
3. Mark Bun
4. Ziteng Sun
5. Daniel M. Kane
6. Jerry Zheng Li
7. Yu Cheng
8. Alistair Stewart
9. Dong Yin
10. Peter Kairouz

Papers from Gautam Kamath's perspective

Ordered by how strongly the researchers Gautam Kamath 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. 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. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Differentially Private Fine-tuning of Language Models (2021) — 517 citations
• Robust Estimators in High Dimensions without the Computational Intractability (2016) — 568 citations
• Remember What You Want to Forget: Algorithms for Machine Unlearning (2021) — 475 citations
• Sever: A Robust Meta-Algorithm for Stochastic Optimization (2018) — 319 citations
• Being Robust (in High Dimensions) Can Be Practical (2017) — 267 citations
• Privately Learning High-Dimensional Distributions (2018) — 176 citations
• The Discrete Gaussian for Differential Privacy (2020) — 345 citations
• The structure of optimal private tests for simple hypotheses (2018) — 90 citations
• Robustly Learning a Gaussian: Getting Optimal Error, Efficiently (2017) — 142 citations
• INSPECTRE: Privately Estimating the Unseen (2018) — 23 citations
• Private Hypothesis Selection (2019) — 101 citations
• Private Mean Estimation of Heavy-Tailed Distributions (2020) — 118 citations
• CoinPress: Practical Private Mean and Covariance Estimation (2020) — 131 citations
• Which Distribution Distances are Sublinearly Testable? (2017) — 50 citations
• Differentially Private Algorithms for Learning Mixtures of Separated Gaussians (2019) — 51 citations
• Priv'IT: Private and Sample Efficient Identity Testing (2017) — 56 citations