Gautam Kamath

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Jerry LiMicrosoft ResearchVerified email at microsoft.com
Jonathan UllmanAssociate Professor of Computer Science, Northeastern UniversityVerified email at ccs.neu.edu
Constantinos DaskalakisProfessor of Computer Science, MITVerified email at csail.mit.edu
Daniel KaneUniversity of California, San DiegoVerified email at ucsd.edu
Ilias DiakonikolasUniversity of Wisconsin-MadisonVerified email at wisc.edu
Alistair StewartWeb3 foundationVerified email at web3.foundation
Clément L. CanonneUniversity of SydneyVerified email at sydney.edu.au
Vikrant SinghalPostdoctoral Fellow, University of WaterlooVerified email at northeastern.edu
Jayadev AcharyaAssistant Professor, Cornell UniversityVerified email at cornell.edu
Ankur MoitraNorbert Wiener Professor of Mathematics, MITVerified email at mit.edu
Janardhan KulkarniMicrosoft Research, RedmondVerified email at cs.washington.edu
Huanyu ZhangMetaVerified email at cornell.edu
Thomas SteinkeGoogle DeepMindVerified email at google.com
Zhiwei Steven WuCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Da YuSun Yat-sen UniversityVerified email at mail2.sysu.edu.cn
Christos TzamosUniversity of Athens and University of Wisconsin-MadisonVerified email at di.uoa.gr
Nishanth DikkalaGoogle ResearchVerified email at google.com
Sivakanth GopiMicrosoft ResearchVerified email at microsoft.com
Sergey YekhaninMicrosoftVerified email at microsoft.com
Huishuai ZhangMicrosoft Research AsiaVerified email at microsoft.com

Gautam Kamath

Other namesGautam C. Kamath

Assistant Professor @ University of Waterloo, Faculty Member @ Vector Institute

Verified email at uwaterloo.ca - Homepage

Statistics Machine Learning Privacy Robustness


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Robust Estimators in High-Dimensions Without the Computational Intractability I Diakonikolas, G Kamath, D Kane, J Li, A Moitra, A Stewart SIAM Journal on Computing 48 (2), 742-864, 2019	441	2019
Sever: A Robust Meta-Algorithm for Stochastic Optimization I Diakonikolas, G Kamath, D Kane, J Li, J Steinhardt, A Stewart Proceedings of the 36th International Conference on Machine Learning, 1596-1606, 2019	268	2019
Being Robust (in High Dimensions) Can Be Practical I Diakonikolas, G Kamath, DM Kane, J Li, A Moitra, A Stewart Proceedings of the 34th International Conference on Machine Learning, 999-1008, 2017	233	2017
The Discrete Gaussian for Differential Privacy C Canonne, G Kamath, T Steinke Advances in Neural Information Processing Systems 33, 2020	166	2020
Optimal Testing for Properties of Distributions J Acharya, C Daskalakis, G Kamath Advances in Neural Information Processing Systems, 3591-3599, 2015	164	2015
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently I Diakonikolas, G Kamath, DM Kane, J Li, A Moitra, A Stewart Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms …, 2018	126	2018
Differentially private fine-tuning of language models D Yu, S Naik, A Backurs, S Gopi, HA Inan, G Kamath, J Kulkarni, YT Lee, ... arXiv preprint arXiv:2110.06500, 2021	113	2021
Privately Learning High-Dimensional Distributions G Kamath, J Li, V Singhal, J Ullman Proceedings of the 32nd Annual Conference on Learning Theory, 1853-1902, 2019	107	2019
Testing Ising Models C Daskalakis, N Dikkala, G Kamath IEEE Transactions on Information Theory 65 (11), 6829-6852, 2019	105	2019
Remember what you want to forget: Algorithms for machine unlearning A Sekhari, J Acharya, G Kamath, AT Suresh Advances in Neural Information Processing Systems 34, 18075-18086, 2021	94	2021
Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians C Daskalakis, G Kamath Proceedings of the 27th Annual Conference on Learning Theory, 1183-1213, 2014	94	2014
An Analysis of One-Dimensional Schelling Segregation C Brandt, N Immorlica, G Kamath, R Kleinberg Proceedings of the 44th Annual ACM Symposium on the Theory of Computing, 789-804, 2012	80	2012
CoinPress: Practical Private Mean and Covariance Estimation S Biswas, Y Dong, G Kamath, J Ullman Advances in Neural Information Processing Systems 33, 2020	76	2020
Private Mean Estimation of Heavy-Tailed Distributions G Kamath, V Singhal, J Ullman Proceedings of the 33rd Annual Conference on Learning Theory, 2204-2235, 2020	73	2020
Private hypothesis selection M Bun, G Kamath, T Steinke, SZ Wu Advances in Neural Information Processing Systems 32, 2019	71	2019
Enabling fast differentially private sgd via just-in-time compilation and vectorization P Subramani, N Vadivelu, G Kamath Advances in Neural Information Processing Systems 34, 26409-26421, 2021	56	2021
The Structure of Optimal Private Tests for Simple Hypotheses CL Canonne, G Kamath, A McMillan, A Smith, J Ullman Proceedings of the 51st Annual ACM Symposium on the Theory of Computing, 310-321, 2019	56	2019
Priv'IT: Private and Sample Efficient Identity Testing B Cai, C Daskalakis, G Kamath Proceedings of the 34th International Conference on Machine Learning, 635-644, 2017	54	2017
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians G Kamath, O Sheffet, V Singhal, J Ullman Advances in Neural Information Processing Systems 32, 168-180, 2019	43	2019
Which Distribution Distances are Sublinearly Testable? C Daskalakis, G Kamath, J Wright Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms …, 2018	43	2018

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