Gautam Kamath
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
43
Venues
10
Active years
2012–2026
Best venue rank
A*
Where they publish
Papers
43 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | AAAI | Demystifying Foreground-Background Memorization in Diffusion Models. | Jimmy Z. Di, Yiwei Lu, Yaoliang Yu, Gautam Kamath, Adam Dziedzic, Franziska Boenisch |
| 2025 | COLT | Optimal Differentially Private Sampling of Unbounded Gaussians. | Valentio Iverson, Gautam Kamath, Argyris Mouzakis |
| 2025 | ICLR | Machine Unlearning Fails to Remove Data Poisoning Attacks. | Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel |
| 2025 | ICML | On the Learnability of Distribution Classes with Adaptive Adversaries. | Tosca Lechner, Alex Bie, Gautam Kamath |
| 2025 | SODA | Private Mean Estimation with Person-Level Differential Privacy. | Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, Jonathan R. Ullman |
| 2024 | ALT | Not All Learnable Distribution Classes are Privately Learnable. | Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal |
| 2024 | ICML | Disguised Copyright Infringement of Latent Diffusion Models. | Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu |
| 2024 | ICML | Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining. | Florian Tramr, Gautam Kamath, Nicholas Carlini |
| 2024 | ICML | Differentially Private Post-Processing for Fair Regression. | Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao |
| 2023 | ICML | Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. | Yiwei Lu, Gautam Kamath, Yaoliang Yu |
| 2023 | STOC | Robustness Implies Privacy in Statistical Estimation. | Samuel B. Hopkins, Gautam Kamath, Mahbod Majid, Shyam Narayanan |
| 2022 | AAAI | The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. | Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar |
| 2022 | COLT | Robust Estimation for Random Graphs. | Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang |
| 2022 | COLT | The Price of Tolerance in Distribution Testing. | Clment L. Canonne, Ayush Jain, Gautam Kamath, Jerry Li |
| 2022 | COLT | A Private and Computationally-Efficient Estimator for Unbounded Gaussians. | Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman |
| 2022 | ICLR | Differentially Private Fine-tuning of Language Models. | 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 | ICML | Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data. | Gautam Kamath, Xingtu Liu, Huanyu Zhang |
| 2022 | ISIT | Calibration with Privacy in Peer Review. | Wenxin Ding, Gautam Kamath, Weina Wang, Nihar B. Shah |
| 2022 | STOC | Efficient mean estimation with pure differential privacy via a sum-of-squares exponential mechanism. | Samuel B. Hopkins, Gautam Kamath, Mahbod Majid |
| 2021 | ALT | On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. | Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath |
| 2021 | ICML | PAPRIKA: Private Online False Discovery Rate Control. | Wanrong Zhang, Gautam Kamath, Rachel Cummings |
| 2021 | SODA | Random Restrictions of High Dimensional Distributions and Uniformity Testing with Subcube Conditioning. | Clment L. Canonne, Xi Chen, Gautam Kamath, Amit Levi, Erik Waingarten |
| 2020 | COLT | Private Mean Estimation of Heavy-Tailed Distributions. | Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman |
| 2020 | COLT | Locally Private Hypothesis Selection. | Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang |
| 2020 | ICML | Privately Learning Markov Random Fields. | Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu |
| 2020 | ITA | Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. | Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman |
| 2019 | COLT | Privately Learning High-Dimensional Distributions. | Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman |
| 2019 | ICML | Sever: A Robust Meta-Algorithm for Stochastic Optimization. | Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart |
| 2019 | SODA | Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing. | Gautam Kamath, Christos Tzamos |
| 2019 | STOC | The structure of optimal private tests for simple hypotheses. | Clment L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman |
| 2018 | COLT | Actively Avoiding Nonsense in Generative Models. | Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, Christos Tzamos |
| 2018 | ICML | INSPECTRE: Privately Estimating the Unseen. | Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang |
| 2018 | SODA | Testing Ising Models. | Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath |
| 2018 | SODA | Which Distribution Distances are Sublinearly Testable? | Constantinos Daskalakis, Gautam Kamath, John Wright |
| 2018 | SODA | Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. | Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart |
| 2017 | ICML | Priv'IT: Private and Sample Efficient Identity Testing. | Bryan Cai, Constantinos Daskalakis, Gautam Kamath |
| 2017 | ICML | Being Robust (in High Dimensions) Can Be Practical. | Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart |
| 2016 | FOCS | Robust Estimators in High Dimensions without the Computational Intractability. | Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart |
| 2016 | STOC | A size-free CLT for poisson multinomials and its applications. | Constantinos Daskalakis, Anindya De, Gautam Kamath, Christos Tzamos |
| 2015 | FOCS | On the Structure, Covering, and Learning of Poisson Multinomial Distributions. | Constantinos Daskalakis, Gautam Kamath, Christos Tzamos |
| 2015 | ISIT | Adaptive estimation in weighted group testing. | Jayadev Acharya, Clment L. Canonne, Gautam Kamath |
| 2014 | COLT | Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians. | Constantinos Daskalakis, Gautam Kamath |
| 2012 | STOC | An analysis of one-dimensional schelling segregation. | Christina Brandt, Nicole Immorlica, Gautam Kamath, Robert Kleinberg |