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Gautam Kamath
Gautam Kamath
Inne imiona/nazwiskaGautam C. Kamath
Assistant Professor @ University of Waterloo, Faculty Member @ Vector Institute
Zweryfikowany adres z uwaterloo.ca - Strona główna
Tytuł
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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
5722019
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
3652022
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
3502019
The Discrete Gaussian for Differential Privacy
C Canonne, G Kamath, T Steinke
Advances in Neural Information Processing Systems 33, 2020
3212020
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
3042021
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
2882017
Optimal Testing for Properties of Distributions
J Acharya, C Daskalakis, G Kamath
Advances in Neural Information Processing Systems, 3591-3599, 2015
1872015
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
1792019
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
1552018
CoinPress: Practical Private Mean and Covariance Estimation
S Biswas, Y Dong, G Kamath, J Ullman
Advances in Neural Information Processing Systems 33, 2020
1292020
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
1202020
Testing Ising Models
C Daskalakis, N Dikkala, G Kamath
IEEE Transactions on Information Theory 65 (11), 6829-6852, 2019
1192019
Private hypothesis selection
M Bun, G Kamath, T Steinke, SZ Wu
Advances in Neural Information Processing Systems 32, 2019
1152019
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
1082014
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
912019
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
892021
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
882012
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
752022
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
692023
Position: Considerations for differentially private learning with large-scale public pretraining
F Tramèr, G Kamath, N Carlini
arXiv preprint arXiv:2212.06470, 2022
65*2022
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