Gautam Kamath

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Jonathan UllmanAssociate Professor of Computer Science, Northeastern UniversityAdresse e-mail validée de ccs.neu.edu
Jerry LiUniversity of WashingtonAdresse e-mail validée de cs.washington.edu
Vikrant SinghalResearch Associate, Harvard UniversityAdresse e-mail validée de seas.harvard.edu
Constantinos DaskalakisProfessor of Computer Science, MITAdresse e-mail validée de csail.mit.edu
Daniel KaneUniversity of California, San DiegoAdresse e-mail validée de ucsd.edu
Ilias DiakonikolasUniversity of Wisconsin-MadisonAdresse e-mail validée de wisc.edu
Alistair StewartWeb3 foundationAdresse e-mail validée de web3.foundation
Clément L. CanonneUniversity of SydneyAdresse e-mail validée de sydney.edu.au
Jayadev AcharyaAssistant Professor, Cornell UniversityAdresse e-mail validée de cornell.edu
Thomas SteinkeGoogle DeepMindAdresse e-mail validée de google.com
Ankur MoitraNorbert Wiener Professor of Mathematics, MITAdresse e-mail validée de mit.edu
Janardhan KulkarniMicrosoft Research, RedmondAdresse e-mail validée de cs.washington.edu
Huanyu ZhangMetaAdresse e-mail validée de cornell.edu
Da YuGoogle ResearchAdresse e-mail validée de google.com
Zhiwei Steven WuCarnegie Mellon UniversityAdresse e-mail validée de andrew.cmu.edu
Sergey YekhaninMicrosoftAdresse e-mail validée de microsoft.com
Argyris MouzakisPhD Student, University of WaterlooAdresse e-mail validée de uwaterloo.ca
Ayush SekhariPostdoctoral Associate, MITAdresse e-mail validée de mit.edu
Sivakanth GopiMicrosoft ResearchAdresse e-mail validée de microsoft.com
Huishuai ZhangPeking UniversityAdresse e-mail validée de pku.edu.cn

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Gautam Kamath

Autres nomsGautam C. Kamath

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

Adresse e-mail validée de uwaterloo.ca - Page d'accueil

Statistics Machine Learning Privacy Robustness


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	558	2019
Differentially Private Fine-tuning of Language Models D Yu, S Naik, A Backurs, S Gopi, HA Inan, G Kamath, J Kulkarni, YT Lee, ... Proceedings of the 2022 International Conference on Learning Representations, 2022	349	2022
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	348	2019
The Discrete Gaussian for Differential Privacy C Canonne, G Kamath, T Steinke Advances in Neural Information Processing Systems 33, 2020	309	2020
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	283	2021
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	283	2017
Optimal Testing for Properties of Distributions J Acharya, C Daskalakis, G Kamath Advances in Neural Information Processing Systems, 3591-3599, 2015	184	2015
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	176	2019
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	151	2018
CoinPress: Practical Private Mean and Covariance Estimation S Biswas, Y Dong, G Kamath, J Ullman Advances in Neural Information Processing Systems 33, 2020	125	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	115	2020
Testing Ising Models C Daskalakis, N Dikkala, G Kamath IEEE Transactions on Information Theory 65 (11), 6829-6852, 2019	114	2019
Private hypothesis selection M Bun, G Kamath, T Steinke, SZ Wu Advances in Neural Information Processing Systems 32, 2019	112	2019
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	106	2014
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	88	2019
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	88	2012
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	87	2021
Efficient mean estimation with pure differential privacy via a sum-of-squares exponential mechanism SB Hopkins, G Kamath, M Majid Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing …, 2022	74	2022
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	64	2019
Robustness implies privacy in statistical estimation SB Hopkins, G Kamath, M Majid, S Narayanan Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 497-506, 2023	62	2023

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