Gautam Kamath - RankMe

155 papers A* 51B 5Misc 1Journal 91Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Demystifying Foreground-Background Memorization in Diffusion Models. AAAI Jimmy Z. Di, Yiwei Lu, Yaoliang Yu, Gautam Kamath, Adam Dziedzic, Franziska Boenisch
2026	J	`jnl`	Rethinking Benchmarks for Differentially Private Image Classification. CoRR Sabrina Mokhtari, Sara Kodeiri, Shubhankar Mohapatra, Florian Tramèr, Gautam Kamath
2025	—	`conf`	Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms Under Composition. SaTML Christian Janos Lebeda, Matthew Regehr, Gautam Kamath, Thomas Steinke
2025	—	`conf`	Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance. SaTML Xin Gu, Gautam Kamath, Zhiwei Steven Wu
2025	J	`jnl`	Demystifying Foreground-Background Memorization in Diffusion Models. CoRR Jimmy Z. Di, Yiwei Lu, Yaoliang Yu, Gautam Kamath, Adam Dziedzic, Franziska Boenisch
2025	B	`ed.`	International Conference on Algorithmic Learning Theory, 24-27 February 2025, Politecnico di Milano, Milan, Italy. ALT Gautam Kamath, Po-Ling Loh
2025	A*	`conf`	Machine Unlearning Fails to Remove Data Poisoning Attacks. ICLR Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel
2025	J	`jnl`	Not All Samples Are Equal: Quantifying Instance-level Difficulty in Targeted Data Poisoning. CoRR William Xu, Yiwei Lu, Yihan Wang, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu
2025	A*	`conf`	On the Learnability of Distribution Classes with Adaptive Adversaries. ICML Tosca Lechner, Alex Bie, Gautam Kamath
2025	J	`jnl`	On the Learnability of Distribution Classes with Adaptive Adversaries. CoRR Tosca Lechner, Alex Bie, Gautam Kamath
2025	A*	`conf`	Optimal Differentially Private Sampling of Unbounded Gaussians. COLT Valentio Iverson, Gautam Kamath, Argyris Mouzakis
2025	J	`jnl`	Optimal Differentially Private Sampling of Unbounded Gaussians. CoRR Valentio Iverson, Gautam Kamath, Argyris Mouzakis
2025	—	`conf`	Position: Membership Inference Attacks Cannot Prove That a Model was Trained on Your Data. SaTML Jie Zhang, Debeshee Das, Gautam Kamath, Florian Tramèr
2025	A*	`conf`	Private Mean Estimation with Person-Level Differential Privacy. SODA Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, Jonathan R. Ullman
2025	J	`jnl`	Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph. CoRR Gautam Kamath, Alireza F. Pour, Matthew Regehr, David P. Woodruff
2024	J	`jnl`	Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition. CoRR Christian Janos Lebeda, Matthew Regehr, Gautam Kamath, Thomas Steinke
2024	J	`jnl`	BridgePure: Revealing the Fragility of Black-box Data Protection. CoRR Yihan Wang, Yiwei Lu, Xiao-Shan Gao, Gautam Kamath, Yaoliang Yu
2024	J	`jnl`	Differentially Private Fine-tuning of Language Models. J. Priv. Confidentiality 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
2024	A*	`conf`	Differentially Private Post-Processing for Fair Regression. ICML Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao
2024	J	`jnl`	Differentially Private Post-Processing for Fair Regression. CoRR Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao
2024	A*	`conf`	Disguised Copyright Infringement of Latent Diffusion Models. ICML Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu
2024	J	`jnl`	Disguised Copyright Infringement of Latent Diffusion Models. CoRR Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu
2024	J	`jnl`	Distribution Learnability and Robustness. CoRR Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner
2024	—	`conf`	Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. SaTML Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath, Yaoliang Yu
2024	J	`jnl`	Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. CoRR Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath, Yaoliang Yu
2024	J	`jnl`	Machine Unlearning Fails to Remove Data Poisoning Attacks. CoRR Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel
2024	J	`jnl`	Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data. CoRR Jie Zhang, Debeshee Das, Gautam Kamath, Florian Tramèr
2024	B	`conf`	Not All Learnable Distribution Classes are Privately Learnable. ALT Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
2024	J	`jnl`	Not All Learnable Distribution Classes are Privately Learnable. CoRR Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
2024	A*	`conf`	Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining. ICML Florian Tramèr, Gautam Kamath, Nicholas Carlini
2024	J	`jnl`	Private Mean Estimation with Person-Level Differential Privacy. CoRR Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, Jonathan R. Ullman
2024	J	`jnl`	The Broader Landscape of Robustness in Algorithmic Statistics. CoRR Gautam Kamath
2023	J	`jnl`	A Bias-Variance-Privacy Trilemma for Statistical Estimation. CoRR Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
2023	J	`jnl`	Challenges towards the Next Frontier in Privacy. CoRR 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
2023	J	`jnl`	Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance. CoRR Xin Gu, Gautam Kamath, Zhiwei Steven Wu
2023	A*	`conf`	Distribution Learnability and Robustness. NeurIPS Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner
2023	J	`jnl`	Exploring the Limits of Indiscriminate Data Poisoning Attacks. CoRR Yiwei Lu, Gautam Kamath, Yaoliang Yu
2023	A*	`conf`	Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. ICML Yiwei Lu, Gautam Kamath, Yaoliang Yu
2023	A*	`conf`	Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks. NeurIPS Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari
2023	J	`jnl`	Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent. Trans. Mach. Learn. Res. Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, Huishuai Zhang
2023	A*	`conf`	Private Distribution Learning with Public Data: The View from Sample Compression. NeurIPS Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal
2023	J	`jnl`	Private Distribution Learning with Public Data: The View from Sample Compression. CoRR Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal
2023	J	`jnl`	Private GANs, Revisited. CoRR Alex Bie, Gautam Kamath, Guojun Zhang
2023	J	`jnl`	Private GANs, Revisited. Trans. Mach. Learn. Res. Alex Bie, Gautam Kamath, Guojun Zhang
2023	J	`jnl`	Report of the 1st Workshop on Generative AI and Law. CoRR 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 A. 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
2023	A*	`conf`	Robustness Implies Privacy in Statistical Estimation. STOC Samuel B. Hopkins, Gautam Kamath, Mahbod Majid, Shyam Narayanan
2022	A*	`conf`	A Private and Computationally-Efficient Estimator for Unbounded Gaussians. COLT Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
2022	B	`conf`	Calibration with Privacy in Peer Review. ISIT Wenxin Ding, Gautam Kamath, Weina Wang, Nihar B. Shah
2022	J	`jnl`	Calibration with Privacy in Peer Review. CoRR Wenxin Ding, Gautam Kamath, Weina Wang, Nihar B. Shah
2022	J	`jnl`	Considerations for Differentially Private Learning with Large-Scale Public Pretraining. CoRR Florian Tramèr, Gautam Kamath, Nicholas Carlini
2022	A*	`conf`	Differentially Private Fine-tuning of Language Models. ICLR 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	J	`jnl`	Discrete Gaussian for Differential Privacy. J. Priv. Confidentiality Clément L. Canonne, Gautam Kamath, Thomas Steinke
2022	A*	`conf`	Efficient mean estimation with pure differential privacy via a sum-of-squares exponential mechanism. STOC Samuel B. Hopkins, Gautam Kamath, Mahbod Majid
2022	J	`jnl`	Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks. CoRR Jimmy Z. Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, Ayush Sekhari
2022	A*	`conf`	Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data. ICML Gautam Kamath, Xingtu Liu, Huanyu Zhang
2022	J	`jnl`	Indiscriminate Data Poisoning Attacks on Neural Networks. CoRR Yiwei Lu, Gautam Kamath, Yaoliang Yu
2022	J	`jnl`	Indiscriminate Data Poisoning Attacks on Neural Networks. Trans. Mach. Learn. Res. Yiwei Lu, Gautam Kamath, Yaoliang Yu
2022	J	`jnl`	Introduction to the Special Issue on ACM-SIAM Symposium on Discrete Algorithms (SODA) 2020. ACM Trans. Algorithms Gautam Kamath, Sepehr Assadi, Anne Driemel, Janardhan Kulkarni
2022	A*	`conf`	New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma. NeurIPS Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
2022	J	`jnl`	New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma. CoRR Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
2022	J	`jnl`	Per-Instance Privacy Accounting for Differentially Private Stochastic Gradient Descent. CoRR Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, Huishuai Zhang
2022	A*	`conf`	Private Estimation with Public Data. NeurIPS Alex Bie, Gautam Kamath, Vikrant Singhal
2022	J	`jnl`	Private Estimation with Public Data. CoRR Alex Bie, Gautam Kamath, Vikrant Singhal
2022	A*	`conf`	Robust Estimation for Random Graphs. COLT Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang
2022	J	`jnl`	Robustness Implies Privacy in Statistical Estimation. CoRR Samuel B. Hopkins, Gautam Kamath, Mahbod Majid, Shyam Narayanan
2022	A*	`conf`	The Price of Tolerance in Distribution Testing. COLT Clément L. Canonne, Ayush Jain, Gautam Kamath, Jerry Li
2022	A*	`conf`	The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. AAAI Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar
2021	J	`jnl`	A Private and Computationally-Efficient Estimator for Unbounded Gaussians. CoRR Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
2021	J	`jnl`	Differentially Private Fine-tuning of Language Models. CoRR 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
2021	J	`jnl`	Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism. CoRR Samuel B. Hopkins, Gautam Kamath, Mahbod Majid
2021	A*	`conf`	Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization. NeurIPS Pranav Subramani, Nicholas Vadivelu, Gautam Kamath
2021	J	`jnl`	Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data. CoRR Gautam Kamath, Xingtu Liu, Huanyu Zhang
2021	B	`conf`	On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. ALT Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath
2021	A*	`conf`	PAPRIKA: Private Online False Discovery Rate Control. ICML Wanrong Zhang, Gautam Kamath, Rachel Cummings
2021	J	`jnl`	Private Hypothesis Selection. IEEE Trans. Inf. Theory Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
2021	A*	`conf`	Random Restrictions of High Dimensional Distributions and Uniformity Testing with Subcube Conditioning. SODA Clément L. Canonne, Xi Chen, Gautam Kamath, Amit Levi, Erik Waingarten
2021	A*	`conf`	Remember What You Want to Forget: Algorithms for Machine Unlearning. NeurIPS Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh
2021	J	`jnl`	Remember What You Want to Forget: Algorithms for Machine Unlearning. CoRR Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh
2021	J	`jnl`	Robust Estimation for Random Graphs. CoRR Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang
2021	J	`jnl`	Robustness meets algorithms. Commun. ACM Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2021	J	`jnl`	The Price of Tolerance in Distribution Testing. CoRR Clément L. Canonne, Ayush Jain, Gautam Kamath, Jerry Li
2021	J	`jnl`	The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection. CoRR Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar
2021	J	`jnl`	Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy. CoRR Christian Covington, Xi He, James Honaker, Gautam Kamath
2020	J	`jnl`	A Primer on Private Statistics. CoRR Gautam Kamath, Jonathan R. Ullman
2020	A*	`conf`	CoinPress: Practical Private Mean and Covariance Estimation. NeurIPS Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan R. Ullman
2020	J	`jnl`	CoinPress: Practical Private Mean and Covariance Estimation. CoRR Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan R. Ullman
2020	Misc	`conf`	Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. ITA Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman
2020	J	`jnl`	Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization. CoRR Pranav Subramani, Nicholas Vadivelu, Gautam Kamath
2020	J	`jnl`	INSPECTRE: Privately Estimating the Unseen. J. Priv. Confidentiality Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang
2020	A*	`conf`	Locally Private Hypothesis Selection. COLT Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
2020	J	`jnl`	Locally Private Hypothesis Selection. CoRR Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
2020	J	`jnl`	On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. CoRR Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath
2020	J	`jnl`	PAPRIKA: Private Online False Discovery Rate Control. CoRR Wanrong Zhang, Gautam Kamath, Rachel Cummings
2020	A*	`conf`	Private Identity Testing for High-Dimensional Distributions. NeurIPS Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou
2020	A*	`conf`	Private Mean Estimation of Heavy-Tailed Distributions. COLT Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman
2020	J	`jnl`	Private Mean Estimation of Heavy-Tailed Distributions. CoRR Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman
2020	A*	`conf`	Privately Learning Markov Random Fields. ICML Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu
2020	J	`jnl`	Privately Learning Markov Random Fields. CoRR Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu
2020	A*	`ed.`	Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, STOC 2020, Chicago, IL, USA, June 22-26, 2020 STOC Konstantin Makarychev, Yury Makarychev, Madhur Tulsiani, Gautam Kamath, Julia Chuzhoy
2020	A*	`conf`	The Discrete Gaussian for Differential Privacy. NeurIPS Clément L. Canonne, Gautam Kamath, Thomas Steinke
2020	J	`jnl`	The Discrete Gaussian for Differential Privacy. CoRR Clément L. Canonne, Gautam Kamath, Thomas Steinke
2019	A*	`conf`	Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing. SODA Gautam Kamath, Christos Tzamos
2019	A*	`conf`	Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. NeurIPS Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman
2019	J	`jnl`	Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. CoRR Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman
2019	A*	`conf`	Private Hypothesis Selection. NeurIPS Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
2019	J	`jnl`	Private Hypothesis Selection. CoRR Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
2019	J	`jnl`	Private Identity Testing for High-Dimensional Distributions. CoRR Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou
2019	A*	`conf`	Privately Learning High-Dimensional Distributions. COLT Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman
2019	J	`jnl`	Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning. CoRR Clément L. Canonne, Xi Chen, Gautam Kamath, Amit Levi, Erik Waingarten
2019	J	`jnl`	Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning. Electron. Colloquium Comput. Complex. Clément L. Canonne, Xi Chen, Gautam Kamath, Amit Levi, Erik Waingarten
2019	J	`jnl`	Robust Estimators in High-Dimensions Without the Computational Intractability. SIAM J. Comput. Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2019	A*	`conf`	Sever: A Robust Meta-Algorithm for Stochastic Optimization. ICML Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart
2019	J	`jnl`	Testing Ising Models. IEEE Trans. Inf. Theory Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath
2019	A*	`conf`	The structure of optimal private tests for simple hypotheses. STOC Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman
2018	J	`jnl`	A Chasm Between Identity and Equivalence Testing with Conditional Queries. Theory Comput. Jayadev Acharya, Clément L. Canonne, Gautam Kamath
2018	A*	`conf`	Actively Avoiding Nonsense in Generative Models. COLT Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, Christos Tzamos
2018	J	`jnl`	Actively Avoiding Nonsense in Generative Models. CoRR Steve Hanneke, Adam Kalai, Gautam Kamath, Christos Tzamos
2018	J	`jnl`	Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing. CoRR Gautam Kamath, Christos Tzamos
2018	J	`jnl`	Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing. Electron. Colloquium Comput. Complex. Gautam Kamath, Christos Tzamos
2018	A*	`conf`	INSPECTRE: Privately Estimating the Unseen. ICML Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang
2018	J	`jnl`	INSPECTRE: Privately Estimating the Unseen. CoRR Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang
2018	J	`jnl`	Privately Learning High-Dimensional Distributions. CoRR Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman
2018	A*	`conf`	Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. SODA Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2018	J	`jnl`	Sever: A Robust Meta-Algorithm for Stochastic Optimization. CoRR Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart
2018	A*	`conf`	Testing Ising Models. SODA Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath
2018	J	`jnl`	The Structure of Optimal Private Tests for Simple Hypotheses. CoRR Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman
2018	A*	`conf`	Which Distribution Distances are Sublinearly Testable? SODA Constantinos Daskalakis, Gautam Kamath, John Wright
2018	J	`jnl`	Which Distribution Distances are Sublinearly Testable? Electron. Colloquium Comput. Complex. Constantinos Daskalakis, Gautam Kamath, John Wright
2017	A*	`conf`	Being Robust (in High Dimensions) Can Be Practical. ICML Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2017	J	`jnl`	Being Robust (in High Dimensions) Can Be Practical. CoRR Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2017	—	`conf`	Concentration of Multilinear Functions of the Ising Model with Applications to Network Data. NIPS Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath
2017	J	`jnl`	Concentration of Multilinear Functions of the Ising Model with Applications to Network Data. CoRR Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath
2017	A*	`conf`	Priv'IT: Private and Sample Efficient Identity Testing. ICML Bryan Cai, Constantinos Daskalakis, Gautam Kamath
2017	J	`jnl`	Priv'IT: Private and Sample Efficient Identity Testing. CoRR Bryan Cai, Constantinos Daskalakis, Gautam Kamath
2017	J	`jnl`	Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. CoRR Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2017	J	`jnl`	Testing Ising Models. Electron. Colloquium Comput. Complex. Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath
2017	J	`jnl`	Which Distribution Distances are Sublinearly Testable? CoRR Constantinos Daskalakis, Gautam Kamath, John Wright
2016	J	`jnl`	A Framework for Testing Properties of Discrete Distributions: Monotonicity, Independence, and More. Tiny Trans. Comput. Sci. Jayadev Acharya, Constantinos Daskalakis, Gautam Kamath
2016	A*	`conf`	A size-free CLT for poisson multinomials and its applications. STOC Constantinos Daskalakis, Anindya De, Gautam Kamath, Christos Tzamos
2016	A*	`conf`	Robust Estimators in High Dimensions without the Computational Intractability. FOCS Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2016	J	`jnl`	Robust Estimators in High Dimensions without the Computational Intractability. CoRR Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Zheng Li, Ankur Moitra, Alistair Stewart
2016	J	`jnl`	Testing Ising Models. CoRR Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath
2015	—	`conf`	A Chasm Between Identity and Equivalence Testing with Conditional Queries. APPROX-RANDOM Jayadev Acharya, Clément L. Canonne, Gautam Kamath
2015	J	`jnl`	A Size-Free CLT for Poisson Multinomials and its Applications. CoRR Constantinos Daskalakis, Anindya De, Gautam Kamath, Christos Tzamos
2015	B	`conf`	Adaptive estimation in weighted group testing. ISIT Jayadev Acharya, Clément L. Canonne, Gautam Kamath
2015	A*	`conf`	On the Structure, Covering, and Learning of Poisson Multinomial Distributions. FOCS Constantinos Daskalakis, Gautam Kamath, Christos Tzamos
2015	J	`jnl`	On the Structure, Covering, and Learning of Poisson Multinomial Distributions. CoRR Constantinos Daskalakis, Gautam Kamath, Christos Tzamos
2015	—	`conf`	Optimal Testing for Properties of Distributions. NIPS Jayadev Acharya, Constantinos Daskalakis, Gautam Kamath
2015	J	`jnl`	Optimal Testing for Properties of Distributions. CoRR Jayadev Acharya, Constantinos Daskalakis, Gautam Kamath
2014	J	`jnl`	A Chasm Between Identity and Equivalence Testing with Conditional Queries. CoRR Jayadev Acharya, Clément L. Canonne, Gautam Kamath
2014	J	`jnl`	A Chasm Between Identity and Equivalence Testing with Conditional Queries. Electron. Colloquium Comput. Complex. Jayadev Acharya, Clément L. Canonne, Gautam Kamath
2014	A*	`conf`	Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians. COLT Constantinos Daskalakis, Gautam Kamath
2013	J	`jnl`	Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians. CoRR Constantinos Daskalakis, Gautam Kamath
2012	J	`jnl`	An Analysis of One-Dimensional Schelling Segregation CoRR Christina Brandt, Nicole Immorlica, Gautam Kamath, Robert D. Kleinberg
2012	A*	`conf`	An analysis of one-dimensional schelling segregation. STOC Christina Brandt, Nicole Immorlica, Gautam Kamath, Robert Kleinberg