Gautam Kamath

ORCID

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 119

Top Venues

Publications

Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
Not All Learnable Distribution Classes are Privately Learnable. ALT (2024)
Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
Not All Learnable Distribution Classes are Privately Learnable. CoRR (2024)
Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, Jonathan R. Ullman
Private Mean Estimation with Person-Level Differential Privacy. CoRR (2024)
Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath, Yaoliang Yu
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. SaTML (2024)
Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner
Distribution Learnability and Robustness. CoRR (2024)
Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao
Differentially Private Post-Processing for Fair Regression. CoRR (2024)
Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu
Disguised Copyright Infringement of Latent Diffusion Models. CoRR (2024)
Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel
Machine Unlearning Fails to Remove Data Poisoning Attacks. CoRR (2024)
Christian Janos Lebeda, Matthew Regehr, Gautam Kamath, Thomas Steinke
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition. CoRR (2024)
Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath, Yaoliang Yu
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. CoRR (2024)
Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks. NeurIPS (2023)
Yiwei Lu, Gautam Kamath, Yaoliang Yu
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. ICML (2023)
Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, Huishuai Zhang
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent. Trans. Mach. Learn. Res. 2023 (2023)
Samuel B. Hopkins, Gautam Kamath, Mahbod Majid, Shyam Narayanan
Robustness Implies Privacy in Statistical Estimation. STOC (2023)
Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner
Distribution Learnability and Robustness. NeurIPS (2023)
Alex Bie, Gautam Kamath, Guojun Zhang
Private GANs, Revisited. CoRR (2023)
Yiwei Lu, Gautam Kamath, Yaoliang Yu
Exploring the Limits of Indiscriminate Data Poisoning Attacks. CoRR (2023)
Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal
Private Distribution Learning with Public Data: The View from Sample Compression. CoRR (2023)
Xin Gu, Gautam Kamath, Zhiwei Steven Wu
Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance. CoRR (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 J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang
Challenges towards the Next Frontier in Privacy. CoRR (2023)
A. Feder Cooper, Katherine Lee, James Grimmelmann, Daphne Ippolito, Christopher Callison-Burch, Christopher A. Choquette-Choo, Niloofar Mireshghallah, Miles Brundage, David Mimno, Madiha Zahrah Choksi, Jack M. Balkin, Nicholas Carlini, Christopher De Sa, Jonathan Frankle, Deep Ganguli, Bryant Gipson, Andres Guadamuz, Swee Leng Harris, Abigail Z. Jacobs, Elizabeth Joh, Gautam Kamath, Mark Lemley, Cass Matthews, Christine McLeavey, Corynne McSherry, Milad Nasr, Paul Ohm, Adam Roberts, Tom Rubin, Pamela Samuelson, Ludwig Schubert, Kristen Vaccaro, Luis Villa, Felix Wu, Elana Zeide
Report of the 1st Workshop on Generative AI and Law. CoRR (2023)
Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal
Private Distribution Learning with Public Data: The View from Sample Compression. NeurIPS (2023)
Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
A Bias-Variance-Privacy Trilemma for Statistical Estimation. CoRR (2023)
Alex Bie, Gautam Kamath, Guojun Zhang
Private GANs, Revisited. Trans. Mach. Learn. Res. 2023 (2023)
Alex Bie, Gautam Kamath, Vikrant Singhal
Private Estimation with Public Data. CoRR (2022)
Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, Huishuai Zhang
Per-Instance Privacy Accounting for Differentially Private Stochastic Gradient Descent. CoRR (2022)
Yiwei Lu, Gautam Kamath, Yaoliang Yu
Indiscriminate Data Poisoning Attacks on Neural Networks. CoRR (2022)
Gautam Kamath, Xingtu Liu, Huanyu Zhang
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data. ICML (2022)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma. NeurIPS (2022)
Samuel B. Hopkins, Gautam Kamath, Mahbod Majid, Shyam Narayanan
Robustness Implies Privacy in Statistical Estimation. CoRR (2022)
Samuel B. Hopkins, Gautam Kamath, Mahbod Majid
Efficient mean estimation with pure differential privacy via a sum-of-squares exponential mechanism. STOC (2022)
Alex Bie, Gautam Kamath, Vikrant Singhal
Private Estimation with Public Data. NeurIPS (2022)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. COLT (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
Differentially Private Fine-tuning of Language Models. ICLR (2022)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma. CoRR (2022)
Gautam Kamath, Sepehr Assadi, Anne Driemel, Janardhan Kulkarni
Introduction to the Special Issue on ACM-SIAM Symposium on Discrete Algorithms (SODA) 2020. ACM Trans. Algorithms 18 (4) (2022)
Yiwei Lu, Gautam Kamath, Yaoliang Yu
Indiscriminate Data Poisoning Attacks on Neural Networks. Trans. Mach. Learn. Res. 2022 (2022)
Clément L. Canonne, Gautam Kamath, Thomas Steinke
Discrete Gaussian for Differential Privacy. J. Priv. Confidentiality 12 (1) (2022)
Clément L. Canonne, Ayush Jain, Gautam Kamath, Jerry Li
The Price of Tolerance in Distribution Testing. COLT (2022)
Wenxin Ding, Gautam Kamath, Weina Wang, Nihar B. Shah
Calibration with Privacy in Peer Review. CoRR (2022)
Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks. CoRR (2022)
Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. AAAI (2022)
Wenxin Ding, Gautam Kamath, Weina Wang, Nihar B. Shah
Calibration with Privacy in Peer Review. ISIT (2022)
Florian Tramèr, Gautam Kamath, Nicholas Carlini
Considerations for Differentially Private Learning with Large-Scale Public Pretraining. CoRR (2022)
Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang
Robust Estimation for Random Graphs. COLT (2022)
Wanrong Zhang, Gautam Kamath, Rachel Cummings
PAPRIKA: Private Online False Discovery Rate Control. ICML (2021)
Clément L. Canonne, Ayush Jain, Gautam Kamath, Jerry Li
The Price of Tolerance in Distribution Testing. CoRR (2021)
Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
Private Hypothesis Selection. IEEE Trans. Inf. Theory 67 (3) (2021)
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
Robustness meets algorithms. Commun. ACM 64 (5) (2021)
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh
Remember What You Want to Forget: Algorithms for Machine Unlearning. CoRR (2021)
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh
Remember What You Want to Forget: Algorithms for Machine Unlearning. NeurIPS (2021)
Christian Covington, Xi He, James Honaker, Gautam Kamath
Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy. CoRR (2021)
Pranav Subramani, Nicholas Vadivelu, Gautam Kamath
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization. NeurIPS (2021)
Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. ALT (2021)
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
Differentially Private Fine-tuning of Language Models. CoRR (2021)
Clément L. Canonne, Xi Chen, Gautam Kamath, Amit Levi, Erik Waingarten
Random Restrictions of High Dimensional Distributions and Uniformity Testing with Subcube Conditioning. SODA (2021)
Samuel B. Hopkins, Gautam Kamath, Mahbod Majid
Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism. CoRR (2021)
Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. CoRR (2021)
Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang
Robust Estimation for Random Graphs. CoRR (2021)
Gautam Kamath, Xingtu Liu, Huanyu Zhang
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data. CoRR (2021)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. CoRR (2021)
Clément L. Canonne, Gautam Kamath, Thomas Steinke
The Discrete Gaussian for Differential Privacy. NeurIPS (2020)
Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan R. Ullman
CoinPress: Practical Private Mean and Covariance Estimation. CoRR (2020)
Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Locally Private Hypothesis Selection. CoRR (2020)
Pranav Subramani, Nicholas Vadivelu, Gautam Kamath
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization. CoRR (2020)
Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman
Private Mean Estimation of Heavy-Tailed Distributions. CoRR (2020)
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu
Privately Learning Markov Random Fields. CoRR (2020)
Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. CoRR (2020)
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu
Privately Learning Markov Random Fields. ICML (2020)
Gautam Kamath, Jonathan R. Ullman
A Primer on Private Statistics. CoRR (2020)
Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman
Private Mean Estimation of Heavy-Tailed Distributions. COLT (2020)
Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang
INSPECTRE: Privately Estimating the Unseen. J. Priv. Confidentiality 10 (2) (2020)
Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou
Private Identity Testing for High-Dimensional Distributions. NeurIPS (2020)
Clément L. Canonne, Gautam Kamath, Thomas Steinke
The Discrete Gaussian for Differential Privacy. CoRR (2020)
Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan R. Ullman
CoinPress: Practical Private Mean and Covariance Estimation. NeurIPS (2020)
Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
Locally Private Hypothesis Selection. COLT (2020)
Wanrong Zhang, Gautam Kamath, Rachel Cummings
PAPRIKA: Private Online False Discovery Rate Control. CoRR (2020)
Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. ITA (2020)
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart
Sever: A Robust Meta-Algorithm for Stochastic Optimization. ICML (2019)
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart
Robust Estimators in High-Dimensions Without the Computational Intractability. SIAM J. Comput. 48 (2) (2019)
Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. NeurIPS (2019)
Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou
Private Identity Testing for High-Dimensional Distributions. CoRR (2019)
Gautam Kamath, Christos Tzamos
Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing. SODA (2019)
Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. CoRR (2019)
Clément L. Canonne, Xi Chen, Gautam Kamath, Amit Levi, Erik Waingarten
Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning. CoRR (2019)
Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman
The structure of optimal private tests for simple hypotheses. STOC (2019)
Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
Private Hypothesis Selection. NeurIPS (2019)
Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman
Privately Learning High-Dimensional Distributions. COLT (2019)
Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
Private Hypothesis Selection. CoRR (2019)
Clément L. Canonne, Xi Chen, Gautam Kamath, Amit Levi, Erik Waingarten
Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning. Electron. Colloquium Comput. Complex. 26 (2019)
Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath
Testing Ising Models. IEEE Trans. Inf. Theory 65 (11) (2019)
Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman
The Structure of Optimal Private Tests for Simple Hypotheses. CoRR (2018)
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. SODA (2018)
Gautam Kamath, Christos Tzamos
Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing. Electron. Colloquium Comput. Complex. 25 (2018)
Gautam Kamath, Christos Tzamos
Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing. CoRR (2018)
Constantinos Daskalakis, Gautam Kamath, John Wright
Which Distribution Distances are Sublinearly Testable? Electron. Colloquium Comput. Complex. 25 (2018)
Steve Hanneke, Adam Kalai, Gautam Kamath, Christos Tzamos
Actively Avoiding Nonsense in Generative Models. CoRR (2018)
Constantinos Daskalakis, Gautam Kamath, John Wright
Which Distribution Distances are Sublinearly Testable? SODA (2018)
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart
Sever: A Robust Meta-Algorithm for Stochastic Optimization. CoRR (2018)
Jayadev Acharya, Clément L. Canonne, Gautam Kamath
A Chasm Between Identity and Equivalence Testing with Conditional Queries. Theory Comput. 14 (1) (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Gautam Kamath