Gautam Kamath

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Referințe bibliografice	5137	4469
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Jonathan UllmanAssociate Professor of Computer Science, Northeastern UniversityAdresă de e-mail confirmată pe ccs.neu.edu
Jerry LiUniversity of WashingtonAdresă de e-mail confirmată pe cs.washington.edu
Vikrant SinghalResearch Associate, Harvard UniversityAdresă de e-mail confirmată pe seas.harvard.edu
Constantinos DaskalakisProfessor of Computer Science, MITAdresă de e-mail confirmată pe csail.mit.edu
Daniel KaneUniversity of California, San DiegoAdresă de e-mail confirmată pe ucsd.edu
Ilias DiakonikolasUniversity of Wisconsin-MadisonAdresă de e-mail confirmată pe wisc.edu
Alistair StewartWeb3 foundationAdresă de e-mail confirmată pe web3.foundation
Clément L. CanonneUniversity of SydneyAdresă de e-mail confirmată pe sydney.edu.au
Jayadev AcharyaAssistant Professor, Cornell UniversityAdresă de e-mail confirmată pe cornell.edu
Thomas SteinkeGoogle DeepMindAdresă de e-mail confirmată pe google.com
Ankur MoitraNorbert Wiener Professor of Mathematics, MITAdresă de e-mail confirmată pe mit.edu
Janardhan KulkarniMicrosoft Research, RedmondAdresă de e-mail confirmată pe cs.washington.edu
Huanyu ZhangMetaAdresă de e-mail confirmată pe cornell.edu
Da YuGoogle ResearchAdresă de e-mail confirmată pe google.com
Zhiwei Steven WuCarnegie Mellon UniversityAdresă de e-mail confirmată pe andrew.cmu.edu
Sergey YekhaninMicrosoftAdresă de e-mail confirmată pe microsoft.com
Argyris MouzakisPhD Student, University of WaterlooAdresă de e-mail confirmată pe uwaterloo.ca
Ayush SekhariPostdoctoral Associate, MITAdresă de e-mail confirmată pe mit.edu
Yiwei LuUniversity of WaterlooAdresă de e-mail confirmată pe uwaterloo.ca
Sivakanth GopiMicrosoft ResearchAdresă de e-mail confirmată pe microsoft.com

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Gautam Kamath

Alte numeGautam C. Kamath

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

Adresă de e-mail confirmată pe uwaterloo.ca - Pagina de pornire

Statistics Machine Learning Privacy Robustness


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
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	571	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	359	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	350	2019
The Discrete Gaussian for Differential Privacy C Canonne, G Kamath, T Steinke Advances in Neural Information Processing Systems 33, 2020	318	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	298	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	288	2017
Optimal Testing for Properties of Distributions J Acharya, C Daskalakis, G Kamath Advances in Neural Information Processing Systems, 3591-3599, 2015	187	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	178	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	154	2018
CoinPress: Practical Private Mean and Covariance Estimation S Biswas, Y Dong, G Kamath, J Ullman Advances in Neural Information Processing Systems 33, 2020	128	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	120	2020
Testing Ising Models C Daskalakis, N Dikkala, G Kamath IEEE Transactions on Information Theory 65 (11), 6829-6852, 2019	119	2019
Private hypothesis selection M Bun, G Kamath, T Steinke, SZ Wu Advances in Neural Information Processing Systems 32, 2019	114	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	107	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	91	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	88	2021
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
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
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	67	2023
Position: Considerations for differentially private learning with large-scale public pretraining F Tramèr, G Kamath, N Carlini arXiv preprint arXiv:2212.06470, 2022	63*	2022

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