Ullman, Jonathan

27 publications

AISTATS 2025 Privacy in Metalearning and Multitask Learning: Modeling and Separations Maryam Aliakbarpour, Konstantina Bairaktari, Adam Smith, Marika Swanberg, Jonathan Ullman

ICLR 2024 Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning Harsh Chaudhari, Giorgio Severi, Alina Oprea, Jonathan Ullman

ICML 2024 How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization Andrew Lowy, Jonathan Ullman, Stephen Wright

COLT 2024 Metalearning with Very Few Samples per Task Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan Ullman

NeurIPS 2024 Private Geometric Median Mahdi Haghifam, Thomas Steinke, Jonathan Ullman

COLT 2024 Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes Naty Peter, Eliad Tsfadia, Jonathan Ullman

TMLR 2023 Fair and Useful Cohort Selection Konstantina Bairaktari, Paul Tsela Langton, Huy Nguyen, Niklas Smedemark-Margulies, Jonathan Ullman

ICML 2023 From Robustness to Privacy and Back Hilal Asi, Jonathan Ullman, Lydia Zakynthinou

COLT 2023 Multitask Learning via Shared Features: Algorithms and Hardness Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan Ullman, Lydia Zakynthinou

ICMLW 2023 TMI! Finetuned Models Spill Secrets from Pretraining John Abascal, Stanley Wu, Alina Oprea, Jonathan Ullman

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

NeurIPS 2021 Covariance-Aware Private Mean Estimation Without Private Covariance Estimation Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, Lydia Zakynthinou

ICML 2021 Leveraging Public Data for Practical Private Query Release Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, Steven Wu

NeurIPS 2020 Auditing Differentially Private Machine Learning: How Private Is Private SGD? Matthew Jagielski, Jonathan Ullman, Alina Oprea

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

ALT 2020 Efficient Private Algorithms for Learning Large-Margin Halfspaces Huy Lê Nguyễn, Jonathan Ullman, Lydia Zakynthinou

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 Private Query Release Assisted by Public Data Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, Steven Wu

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

ICML 2019 Differentially Private Fair Learning Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman

NeurIPS 2019 Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy Jonathan Ullman, Adam Sealfon

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

NeurIPS 2018 Local Differential Privacy for Evolving Data Matthew Joseph, Aaron Roth, Jonathan Ullman, Bo Waggoner

NeurIPS 2018 The Limits of Post-Selection Generalization Jonathan Ullman, Adam Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke

COLT 2017 The Price of Selection in Differential Privacy Mitali Bafna, Jonathan Ullman

NeurIPS 2016 Privacy Odometers and Filters: Pay-as-You-Go Composition Ryan M Rogers, Aaron Roth, Jonathan Ullman, Salil Vadhan