An analysis of one-dimensional schelling segregation. - Christina Brandt; Nicole Immorlica; Gautam Kamath 001; Robert Kleinberg (STOC, 2012)

Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians. - Constantinos Daskalakis; Gautam Kamath 001 (COLT, 2014)

A Chasm Between Identity and Equivalence Testing with Conditional Queries. - Jayadev Acharya; Clément L. Canonne; Gautam Kamath 001 (Electron. Colloquium Comput. Complex., 2014)

On the Structure, Covering, and Learning of Poisson Multinomial Distributions. - Constantinos Daskalakis; Gautam Kamath 001; Christos Tzamos (FOCS, 2015)

Adaptive estimation in weighted group testing. - Jayadev Acharya; Clément L. Canonne; Gautam Kamath 001 (ISIT, 2015)

Optimal Testing for Properties of Distributions. - Jayadev Acharya; Constantinos Daskalakis; Gautam Kamath 001 (NIPS, 2015)

A size-free CLT for poisson multinomials and its applications. - Constantinos Daskalakis; Anindya De; Gautam Kamath 001; Christos Tzamos (STOC, 2016)

A Framework for Testing Properties of Discrete Distributions: Monotonicity, Independence, and More. - Jayadev Acharya; Constantinos Daskalakis; Gautam Kamath 001 (Adv. Math. Commun., 2016)

Priv'IT: Private and Sample Efficient Identity Testing. - Bryan Cai; Constantinos Daskalakis; Gautam Kamath 001 (ICML, 2017)

Being Robust (in High Dimensions) Can Be Practical. - Ilias Diakonikolas; Gautam Kamath 001; Daniel M. Kane; Jerry Li 001; Ankur Moitra; Alistair Stewart (ICML, 2017)

Concentration of Multilinear Functions of the Ising Model with Applications to Network Data. - Constantinos Daskalakis; Nishanth Dikkala; Gautam Kamath 001 (NIPS, 2017)

Testing Ising Models. - Constantinos Daskalakis; Nishanth Dikkala; Gautam Kamath 001 (Electron. Colloquium Comput. Complex., 2017)

Actively Avoiding Nonsense in Generative Models. - Steve Hanneke; Adam Tauman Kalai; Gautam Kamath 001; Christos Tzamos (COLT, 2018)

Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. - Ilias Diakonikolas; Gautam Kamath 001; Daniel M. Kane; Jerry Li 001; Ankur Moitra; Alistair Stewart (SODA, 2018)

Which Distribution Distances are Sublinearly Testable? - Constantinos Daskalakis; Gautam Kamath 001; John Wright 004 (Electron. Colloquium Comput. Complex., 2018)

Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing. - Gautam Kamath 001; Christos Tzamos (Electron. Colloquium Comput. Complex., 2018)

Modern challenges in distribution testing. - Gautam Chetan Kamath (2018)

Privately Learning High-Dimensional Distributions. - Gautam Kamath 001; Jerry Li 001; Vikrant Singhal; Jonathan R. Ullman (COLT, 2019)

Sever: A Robust Meta-Algorithm for Stochastic Optimization. - Ilias Diakonikolas; Gautam Kamath 001; Daniel Kane 001; Jerry Li 001; Jacob Steinhardt; Alistair Stewart (ICML, 2019)

Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. - Gautam Kamath 001; Or Sheffet; Vikrant Singhal; Jonathan R. Ullman (NeurIPS, 2019)

The structure of optimal private tests for simple hypotheses. - Clément L. Canonne; Gautam Kamath 001; Audra McMillan; Adam D. Smith 001; Jonathan R. Ullman (STOC, 2019)

Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning. - Clément L. Canonne; Xi Chen 001; Gautam Kamath 001; Amit Levi; Erik Waingarten (Electron. Colloquium Comput. Complex., 2019)

Robust Estimators in High-Dimensions Without the Computational Intractability. - Ilias Diakonikolas; Gautam Kamath 001; Daniel Kane 001; Jerry Li 001; Ankur Moitra; Alistair Stewart (SIAM J. Comput., 2019)

Private Mean Estimation of Heavy-Tailed Distributions. - Gautam Kamath 001; Vikrant Singhal; Jonathan R. Ullman (COLT, 2020)

Locally Private Hypothesis Selection. - Sivakanth Gopi; Gautam Kamath 001; Janardhan Kulkarni; Aleksandar Nikolov; Zhiwei Steven Wu; Huanyu Zhang (COLT, 2020)

Privately Learning Markov Random Fields. - Huanyu Zhang; Gautam Kamath 001; Janardhan Kulkarni; Zhiwei Steven Wu (ICML, 2020)

CoinPress: Practical Private Mean and Covariance Estimation. - Sourav Biswas 001; Yihe Dong; Gautam Kamath 001; Jonathan R. Ullman (NeurIPS, 2020)

Private Identity Testing for High-Dimensional Distributions. - Clément L. Canonne; Gautam Kamath 001; Audra McMillan; Jonathan R. Ullman; Lydia Zakynthinou (NeurIPS, 2020)

A Primer on Private Statistics. - Gautam Kamath 001; Jonathan R. Ullman (CoRR, 2020)

INSPECTRE: Privately Estimating the Unseen. - Jayadev Acharya; Gautam Kamath 001; Ziteng Sun; Huanyu Zhang (J. Priv. Confidentiality, 2020)

On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. - Ishaq Aden-Ali; Hassan Ashtiani; Gautam Kamath 001 (ALT, 2021)

PAPRIKA: Private Online False Discovery Rate Control. - Wanrong Zhang 001; Gautam Kamath 001; Rachel Cummings (ICML, 2021)

Remember What You Want to Forget: Algorithms for Machine Unlearning. - Ayush Sekhari; Jayadev Acharya; Gautam Kamath 001; Ananda Theertha Suresh (NeurIPS, 2021)

Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization. - Pranav Subramani; Nicholas Vadivelu; Gautam Kamath 001 (NeurIPS, 2021)

Robustness meets algorithms. - Ilias Diakonikolas; Gautam Kamath 001; Daniel M. Kane; Jerry Li 001; Ankur Moitra; Alistair Stewart (Commun. ACM, 2021)

Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy. - Christian Covington; Xi He 001; James Honaker; Gautam Kamath 001 (CoRR, 2021)

Private Hypothesis Selection. - Mark Bun; Gautam Kamath 001; Thomas Steinke 002; Zhiwei Steven Wu (IEEE Trans. Inf. Theory, 2021)

The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. - Shubhankar Mohapatra; Sajin Sasy; Xi He 001; Gautam Kamath 001; Om Thakkar 001 (AAAI, 2022)

Robust Estimation for Random Graphs. - Jayadev Acharya; Ayush Jain 001; Gautam Kamath 001; Ananda Theertha Suresh; Huanyu Zhang (COLT, 2022)

The Price of Tolerance in Distribution Testing. - Clément L. Canonne; Ayush Jain 001; Gautam Kamath 001; Jerry Li 001 (COLT, 2022)

A Private and Computationally-Efficient Estimator for Unbounded Gaussians. - Gautam Kamath 001; Argyris Mouzakis; Vikrant Singhal; Thomas Steinke 002; Jonathan R. Ullman (COLT, 2022)

Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data. - Gautam Kamath 001; Xingtu Liu 001; Huanyu Zhang (ICML, 2022)

Calibration with Privacy in Peer Review. - Wenxin Ding; Gautam Kamath 001; Weina Wang 001; Nihar B. Shah (ISIT, 2022)

New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma. - Gautam Kamath 001; Argyris Mouzakis; Vikrant Singhal (NeurIPS, 2022)

Private Estimation with Public Data. - Alex Bie; Gautam Kamath 001; Vikrant Singhal (NeurIPS, 2022)

Efficient mean estimation with pure differential privacy via a sum-of-squares exponential mechanism. - Samuel B. Hopkins 001; Gautam Kamath 001; Mahbod Majid (STOC, 2022)

Per-Instance Privacy Accounting for Differentially Private Stochastic Gradient Descent. - Da Yu; Gautam Kamath 001; Janardhan Kulkarni; Tie-Yan Liu; Jian Yin 001; Huishuai Zhang (CoRR, 2022)

Considerations for Differentially Private Learning with Large-Scale Public Pretraining. - Florian Tramèr; Gautam Kamath 001; Nicholas Carlini (CoRR, 2022)

Discrete Gaussian for Differential Privacy. - Clément L. Canonne; Gautam Kamath 001; Thomas Steinke 002 (J. Priv. Confidentiality, 2022)

Introduction to the Special Issue on ACM-SIAM Symposium on Discrete Algorithms (SODA) 2020. - Gautam Kamath 001; Sepehr Assadi; Anne Driemel; Janardhan Kulkarni (ACM Trans. Algorithms, 2022)

Indiscriminate Data Poisoning Attacks on Neural Networks. - Yiwei Lu 001; Gautam Kamath 001; Yaoliang Yu (Trans. Mach. Learn. Res., 2022)

Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. - Yiwei Lu 001; Gautam Kamath 001; Yaoliang Yu (ICML, 2023)

Distribution Learnability and Robustness. - Shai Ben-David; Alex Bie; Gautam Kamath 001; Tosca Lechner (NeurIPS, 2023)

Private Distribution Learning with Public Data: The View from Sample Compression. - Shai Ben-David; Alex Bie; Clément L. Canonne; Gautam Kamath 001; Vikrant Singhal (NeurIPS, 2023)

Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks. - Jimmy Z. Di; Jack Douglas; Jayadev Acharya; Gautam Kamath 001; Ayush Sekhari (NeurIPS, 2023)

Robustness Implies Privacy in Statistical Estimation. - Samuel B. Hopkins 001; Gautam Kamath 001; Mahbod Majid; Shyam Narayanan (STOC, 2023)

A Bias-Variance-Privacy Trilemma for Statistical Estimation. - Gautam Kamath 001; Argyris Mouzakis; Matthew Regehr; Vikrant Singhal; Thomas Steinke 002; Jonathan R. Ullman (CoRR, 2023)

Exploring the Limits of Indiscriminate Data Poisoning Attacks. - Yiwei Lu 001; Gautam Kamath 001; Yaoliang Yu (CoRR, 2023)

Challenges towards the Next Frontier in Privacy. - Rachel Cummings; Damien Desfontaines; David Evans 001; Roxana Geambasu; Matthew Jagielski; Yangsibo Huang; Peter Kairouz; Gautam Kamath 001; Sewoong Oh; Olga Ohrimenko; Nicolas Papernot; Ryan Rogers 002; et al. (CoRR, 2023)

Report of the 1st Workshop on Generative AI and Law. - 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; et al. (CoRR, 2023)

Private GANs, Revisited. - Alex Bie; Gautam Kamath 001; Guojun Zhang (Trans. Mach. Learn. Res., 2023)

Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent. - Da Yu; Gautam Kamath 001; Janardhan Kulkarni; Tie-Yan Liu; Jian Yin 001; Huishuai Zhang (Trans. Mach. Learn. Res., 2023)

Not All Learnable Distribution Classes are Privately Learnable. - Mark Bun; Gautam Kamath 001; Argyris Mouzakis; Vikrant Singhal (ALT, 2024)

Disguised Copyright Infringement of Latent Diffusion Models. - Yiwei Lu 001; Matthew Y. R. Yang; Zuoqiu Liu; Gautam Kamath 001; Yaoliang Yu (ICML, 2024)

Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining. - Florian Tramèr; Gautam Kamath 001; Nicholas Carlini (ICML, 2024)

Differentially Private Post-Processing for Fair Regression. - Ruicheng Xian; Qiaobo Li; Gautam Kamath 001; Han Zhao 002 (ICML, 2024)

Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. - Yiwei Lu 001; Matthew Y. R. Yang; Gautam Kamath 001; Yaoliang Yu (SaTML, 2024)

Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data. - Jie Zhang 107; Debeshee Das; Gautam Kamath 001; Florian Tramèr (CoRR, 2024)

The Broader Landscape of Robustness in Algorithmic Statistics. - Gautam Kamath 001 (CoRR, 2024)

BridgePure: Revealing the Fragility of Black-box Data Protection. - Yihan Wang; Yiwei Lu 001; Xiao-Shan Gao; Gautam Kamath 001; Yaoliang Yu (CoRR, 2024)

Differentially Private Fine-tuning of Language Models. - Da Yu; Saurabh Naik; Arturs Backurs; Sivakanth Gopi; Huseyin A. Inan; Gautam Kamath 001; Janardhan Kulkarni; Yin Tat Lee; Andre Manoel; Lukas Wutschitz; Sergey Yekhanin; Huishuai Zhang (J. Priv. Confidentiality, 2024)

Optimal Differentially Private Sampling of Unbounded Gaussians. - Valentio Iverson; Gautam Kamath 001; Argyris Mouzakis (COLT, 2025)

Machine Unlearning Fails to Remove Data Poisoning Attacks. - Martin Pawelczyk; Jimmy Z. Di; Yiwei Lu 001; Gautam Kamath 001; Ayush Sekhari; Seth Neel (ICLR, 2025)

Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance. - Xin Gu; Gautam Kamath 001; Zhiwei Steven Wu (SaTML, 2025)

Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms Under Composition. - Christian Janos Lebeda; Matthew Regehr; Gautam Kamath 001; Thomas Steinke 002 (SaTML, 2025)

Position: Membership Inference Attacks Cannot Prove That a Model was Trained on Your Data. - Jie Zhang 107; Debeshee Das; Gautam Kamath 001; Florian Tramèr (SaTML, 2025)

Private Mean Estimation with Person-Level Differential Privacy. - Sushant Agarwal; Gautam Kamath 001; Mahbod Majid; Argyris Mouzakis; Rose Silver; Jonathan R. Ullman (SODA, 2025)