Kamath, Gautam

52 publications

ALT 2025 Algorithmic Learning Theory 2025: Preface Gautam Kamath, Po-Ling Loh

NeurIPS 2025 BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection Yihan Wang, Yiwei Lu, Xiao-Shan Gao, Gautam Kamath, Yaoliang Yu

ICLR 2025 Machine Unlearning Fails to Remove Data Poisoning Attacks Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel

ICML 2025 On the Learnability of Distribution Classes with Adaptive Adversaries Tosca Lechner, Alex Bie, Gautam Kamath

COLT 2025 Optimal Differentially Private Sampling of Unbounded Gaussians Valentio Iverson, Gautam Kamath, Argyris Mouzakis

NeurIPS 2025 Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph Gautam Kamath, Alireza F. Pour, Matthew Regehr, David Woodruff

ICML 2024 Differentially Private Post-Processing for Fair Regression Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao

ICML 2024 Disguised Copyright Infringement of Latent Diffusion Models Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu

ALT 2024 Not All Learnable Distribution Classes Are Privately Learnable Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal

ICML 2024 Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining Florian Tramèr, Gautam Kamath, Nicholas Carlini

NeurIPS 2023 Distribution Learnability and Robustness Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner

ICML 2023 Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks Yiwei Lu, Gautam Kamath, Yaoliang Yu

NeurIPS 2023 Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks Jimmy Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari

TMLR 2023 Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, Huishuai Zhang

NeurIPS 2023 Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David, Alex Bie, Clément L Canonne, Gautam Kamath, Vikrant Singhal

TMLR 2023 Private GANs, Revisited Alex Bie, Gautam Kamath, Guojun Zhang

COLT 2022 A Private and Computationally-Efficient Estimator for Unbounded Gaussians Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan Ullman

NeurIPSW 2022 Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance Xin Gu, Gautam Kamath, Steven Wu

ICLR 2022 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

NeurIPSW 2022 Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari

NeurIPSW 2022 Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari

ICML 2022 Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data Gautam Kamath, Xingtu Liu, Huanyu Zhang

TMLR 2022 Indiscriminate Data Poisoning Attacks on Neural Networks Yiwei Lu, Gautam Kamath, Yaoliang Yu

NeurIPSW 2022 Indiscriminate Data Poisoning Attacks on Neural Networks Yiwei Lu, Gautam Kamath, Yaoliang Yu

NeurIPSW 2022 Indiscriminate Data Poisoning Attacks on Neural Networks Yiwei Lu, Gautam Kamath, Yaoliang Yu

NeurIPS 2022 New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma Gautam Kamath, Argyris Mouzakis, Vikrant Singhal

NeurIPS 2022 Private Estimation with Public Data Alex Bie, Gautam Kamath, Vikrant Singhal

NeurIPSW 2022 Private GANs, Revisited Alex Bie, Gautam Kamath, Guojun Zhang

COLT 2022 Robust Estimation for Random Graphs Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang

COLT 2022 The Price of Tolerance in Distribution Testing Clement L Canonne, Ayush Jain, Gautam Kamath, Jerry Li

AAAI 2022 The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar

NeurIPS 2021 Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization Pranav Subramani, Nicholas Vadivelu, Gautam Kamath

ALT 2021 On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath

ICML 2021 PAPRIKA: Private Online False Discovery Rate Control Wanrong Zhang, Gautam Kamath, Rachel Cummings

NeurIPS 2021 Remember What You Want to Forget: Algorithms for Machine Unlearning Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh

NeurIPS 2020 CoinPress: Practical Private Mean and Covariance Estimation Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan Ullman

COLT 2020 Locally Private Hypothesis Selection Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang

NeurIPS 2020 Private Identity Testing for High-Dimensional Distributions Clément L Canonne, Gautam Kamath, Audra McMillan, Jonathan Ullman, Lydia Zakynthinou

COLT 2020 Private Mean Estimation of Heavy-Tailed Distributions Gautam Kamath, Vikrant Singhal, Jonathan Ullman

ICML 2020 Privately Learning Markov Random Fields Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Steven Wu

NeurIPS 2020 The Discrete Gaussian for Differential Privacy Clément L Canonne, Gautam Kamath, Thomas Steinke

NeurIPS 2019 Differentially Private Algorithms for Learning Mixtures of Separated Gaussians Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan Ullman

NeurIPS 2019 Private Hypothesis Selection Mark Bun, Gautam Kamath, Thomas Steinke, Steven Z. Wu

COLT 2019 Privately Learning High-Dimensional Distributions Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan Ullman

ICML 2019 Sever: A Robust Meta-Algorithm for Stochastic Optimization Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart

COLT 2018 Actively Avoiding Nonsense in Generative Models Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, Christos Tzamos

ICML 2018 INSPECTRE: Privately Estimating the Unseen Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang

ICML 2017 Being Robust (in High Dimensions) Can Be Practical Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart

NeurIPS 2017 Concentration of Multilinear Functions of the Ising Model with Applications to Network Data Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath

ICML 2017 Priv’IT: Private and Sample Efficient Identity Testing Bryan Cai, Constantinos Daskalakis, Gautam Kamath

NeurIPS 2015 Optimal Testing for Properties of Distributions Jayadev Acharya, Constantinos Daskalakis, Gautam Kamath

COLT 2014 Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians Constantinos Daskalakis, Gautam Kamath