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Gautam Kamath
Gautam Kamath
Assistant Professor, University of Waterloo
uwaterloo.ca의 이메일 확인됨 - 홈페이지
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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
3562019
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
2102019
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
1912017
Optimal Testing for Properties of Distributions
J Acharya, C Daskalakis, G Kamath
Advances in Neural Information Processing Systems, 3591-3599, 2015
1432015
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
1102018
Testing Ising Models
C Daskalakis, N Dikkala, G Kamath
IEEE Transactions on Information Theory 65 (11), 6829-6852, 2019
882019
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
862014
The Discrete Gaussian for Differential Privacy
C Canonne, G Kamath, T Steinke
Advances in Neural Information Processing Systems 33, 2020
842020
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
742012
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
672019
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
482017
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
462020
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
462019
Private hypothesis selection
M Bun, G Kamath, T Steinke, SZ Wu
Advances in Neural Information Processing Systems 32, 2019
412019
CoinPress: Practical Private Mean and Covariance Estimation
S Biswas, Y Dong, G Kamath, J Ullman
Advances in Neural Information Processing Systems 33, 2020
392020
Which Distribution Distances are Sublinearly Testable?
C Daskalakis, G Kamath, J Wright
Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms …, 2018
362018
A Size-Free CLT for Poisson Multinomials and its Applications
C Daskalakis, A De, G Kamath, C Tzamos
Proceedings of the 48th Annual ACM Symposium on the Theory of Computing …, 2016
362016
A Chasm Between Identity and Equivalence Testing with Conditional Queries
J Acharya, CL Canonne, G Kamath
Theory of Computing 14 (19), 1-46, 2018
32*2018
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
302021
Bounds on the Expectation of the Maximum of Samples from a Gaussian
G Kamath
http://www.gautamkamath.com/writings/gaussian_max.pdf, 2015
302015
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