| 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 | 405 | 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 | 243 | 2019 |
| 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 | 216 | 2017 |
| Optimal Testing for Properties of Distributions J Acharya, C Daskalakis, G Kamath Advances in Neural Information Processing Systems, 3591-3599, 2015 | 156 | 2015 |
| The Discrete Gaussian for Differential Privacy C Canonne, G Kamath, T Steinke Advances in Neural Information Processing Systems 33, 2020 | 134 | 2020 |
| 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 | 118 | 2018 |
| Testing Ising Models C Daskalakis, N Dikkala, G Kamath IEEE Transactions on Information Theory 65 (11), 6829-6852, 2019 | 97 | 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 | 90 | 2014 |
| 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 | 85 | 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 | 77 | 2012 |
| 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 | 64 | 2020 |
| Private hypothesis selection M Bun, G Kamath, T Steinke, SZ Wu Advances in Neural Information Processing Systems 32, 2019 | 61 | 2019 |
| Differentially private fine-tuning of language models D Yu, S Naik, A Backurs, S Gopi, HA Inan, G Kamath, J Kulkarni, YT Lee, ... arXiv preprint arXiv:2110.06500, 2021 | 59 | 2021 |
| CoinPress: Practical Private Mean and Covariance Estimation S Biswas, Y Dong, G Kamath, J Ullman Advances in Neural Information Processing Systems 33, 2020 | 58 | 2020 |
| 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 | 55 | 2019 |
| 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 | 52 | 2021 |
| 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 | 50 | 2017 |
| 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 | 47 | 2021 |
| Which Distribution Distances are Sublinearly Testable? C Daskalakis, G Kamath, J Wright Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms …, 2018 | 40 | 2018 |
| 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 | 38 | 2016 |
Jerry LiMicrosoft ResearchVerified email at microsoft.com
