Gautam Kamath

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Jonathan UllmanAssociate Professor of Computer Science, Northeastern UniversityDirección de correo verificada de ccs.neu.edu
Jerry LiUniversity of WashingtonDirección de correo verificada de cs.washington.edu
Vikrant SinghalResearch Associate, Harvard UniversityDirección de correo verificada de seas.harvard.edu
Constantinos DaskalakisProfessor of Computer Science, MITDirección de correo verificada de csail.mit.edu
Daniel KaneUniversity of California, San DiegoDirección de correo verificada de ucsd.edu
Ilias DiakonikolasUniversity of Wisconsin-MadisonDirección de correo verificada de wisc.edu
Alistair StewartWeb3 foundationDirección de correo verificada de web3.foundation
Clément L. CanonneUniversity of SydneyDirección de correo verificada de sydney.edu.au
Jayadev AcharyaAssistant Professor, Cornell UniversityDirección de correo verificada de cornell.edu
Thomas SteinkeGoogle DeepMindDirección de correo verificada de google.com
Ankur MoitraNorbert Wiener Professor of Mathematics, MITDirección de correo verificada de mit.edu
Janardhan KulkarniMicrosoft Research, RedmondDirección de correo verificada de cs.washington.edu
Huanyu ZhangMetaDirección de correo verificada de cornell.edu
Da YuGoogle ResearchDirección de correo verificada de google.com
Zhiwei Steven WuCarnegie Mellon UniversityDirección de correo verificada de andrew.cmu.edu
Sergey YekhaninMicrosoftDirección de correo verificada de microsoft.com
Argyris MouzakisPhD Student, University of WaterlooDirección de correo verificada de uwaterloo.ca
Ayush SekhariPostdoctoral Associate, MITDirección de correo verificada de mit.edu
Sivakanth GopiMicrosoft ResearchDirección de correo verificada de microsoft.com
Huishuai ZhangPeking UniversityDirección de correo verificada de pku.edu.cn

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Gautam Kamath

Otros nombresGautam C. Kamath

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

Dirección de correo verificada de uwaterloo.ca - Página principal

Statistics Machine Learning Privacy Robustness


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	556	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	348	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	340	2022
The Discrete Gaussian for Differential Privacy C Canonne, G Kamath, T Steinke Advances in Neural Information Processing Systems 33, 2020	304	2020
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	282	2017
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	276	2021
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	174	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	150	2018
CoinPress: Practical Private Mean and Covariance Estimation S Biswas, Y Dong, G Kamath, J Ullman Advances in Neural Information Processing Systems 33, 2020	122	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	114	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	111	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	87	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	85	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	73	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	65	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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