Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner
(2023).
Distribution Learnability and Robustness.
Advances in Neural Information Processing Systems 36 (NeurIPS 2023).
Alex Bie, Gautam Kamath, Guojun Zhang
(2023).
Private GANs, Revisited.
Transactions on Machine Learning Research.
Samuel B Hopkins, Gautam Kamath, Mahbod Majid, Shyam Narayanan
(2023).
Robustness Implies Privacy in Statistical Estimation.
Proceedings of the 55th Annual ACM Symposium on the Theory of Computing (STOC 2023).
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang
(2023).
Challenges towards the Next Frontier in Privacy.
arXiv preprint arXiv:2304.06929.
Alex Bie, Gautam Kamath, Vikrant Singhal
(2022).
Private Estimation with Public Data.
Advances in Neural Information Processing Systems 35 (NeurIPS 2022).
Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang
(2022).
Robust Estimation for Random Graphs.
Proceedings of the 35th Annual Conference on Learning Theory (COLT 2022).
Wenxin Ding, Gautam Kamath, Weina Wang, Nihar B. Shah
(2022).
Calibration with Privacy in Peer Review.
Proceedings of the 2022 IEEE International Symposium on Information Theory (ISIT 2022).
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
(2022).
Differentially Private Fine-tuning of Language Models.
Proceedings of the 10th International Conference on Learning Representations (ICLR 2022).
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
(2021).
Robustness Meets Algorithms.
Communications of the ACM.
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu
(2020).
Privately Learning Markov Random Fields.
Proceedings of the 37th International Conference on Machine Learning (ICML 2020).
Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
(2020).
Locally Private Hypothesis Selection.
Proceedings of the 33rd Annual Conference on Learning Theory (COLT 2020).
Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
(2019).
Private Hypothesis Selection.
Advances in Neural Information Processing Systems 32 (NeurIPS 2019).
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart
(2019).
Sever: A Robust Meta-Algorithm for Stochastic Optimization.
Proceedings of the 36th International Conference on Machine Learning (ICML 2019).
Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang
(2018).
INSPECTRE: Privately Estimating the Unseen.
Proceedings of the 35th International Conference on Machine Learning (ICML 2018).
Steve Hanneke, Adam Kalai, Gautam Kamath, Christos Tzamos
(2018).
Actively Avoiding Nonsense in Generative Models.
Proceedings of the 31st Annual Conference on Learning Theory (COLT 2018).
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
(2017).
Being Robust (in High Dimensions) Can Be Practical.
Proceedings of the 34th International Conference on Machine Learning (ICML 2017).
Jayadev Acharya, Clément L. Canonne, Gautam Kamath
(2015).
Adaptive Estimation in Weighted Group Testing.
Proceedings of the 2015 IEEE International Symposium on Information Theory (ISIT 2015).
Christina Brandt, Nicole Immorlica, Gautam Kamath, Robert Kleinberg
(2012).
An Analysis of One-Dimensional Schelling Segregation.
Proceedings of the 44th Annual ACM Symposium on the Theory of Computing (STOC 2012).