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).
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).