| 2025 | AISTATS | Privacy in Metalearning and Multitask Learning: Modeling and Separations. | Maryam Aliakbarpour, Konstantina Bairaktari, Adam Smith, Marika Swanberg, Jonathan R. Ullman |
| 2025 | SODA | Private Mean Estimation with Person-Level Differential Privacy. | Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, Jonathan R. Ullman |
| 2024 | COLT | Metalearning with Very Few Samples Per Task. | Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman |
| 2024 | COLT | Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes. | Naty Peter, Eliad Tsfadia, Jonathan R. Ullman |
| 2024 | ICLR | Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning. | Harsh Chaudhari, Giorgio Severi, Alina Oprea, Jonathan R. Ullman |
| 2024 | ICML | How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. | Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright |
| 2023 | COLT | Multitask Learning via Shared Features: Algorithms and Hardness. | Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou |
| 2023 | ICML | From Robustness to Privacy and Back. | Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou |
| 2023 | SP | SNAP: Efficient Extraction of Private Properties with Poisoning. | Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramr, Jonathan R. Ullman |
| 2022 | COLT | A Private and Computationally-Efficient Estimator for Unbounded Gaussians. | Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman |
| 2021 | ICML | Leveraging Public Data for Practical Private Query Release. | Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu |
| 2021 | STOC | The limits of pan privacy and shuffle privacy for learning and estimation. | Albert Cheu, Jonathan R. Ullman |
| 2021 | SP | Manipulation Attacks in Local Differential Privacy. | Albert Cheu, Adam D. Smith, Jonathan R. Ullman |
| 2020 | ALT | Efficient Private Algorithms for Learning Large-Margin Halfspaces. | Huy Le Nguyen, Jonathan R. Ullman, Lydia Zakynthinou |
| 2020 | COLT | Private Mean Estimation of Heavy-Tailed Distributions. | Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman |
| 2020 | ICML | Private Query Release Assisted by Public Data. | Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu |
| 2020 | ITA | Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. | Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman |
| 2020 | STOC | The power of factorization mechanisms in local and central differential privacy. | Alexander Edmonds, Aleksandar Nikolov, Jonathan R. Ullman |
| 2019 | CCS | Securely Sampling Biased Coins with Applications to Differential Privacy. | Jeffrey Champion, Abhi Shelat, Jonathan R. Ullman |
| 2019 | COLT | Privately Learning High-Dimensional Distributions. | Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman |
| 2019 | EuroCrypt | Distributed Differential Privacy via Shuffling. | Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev |
| 2019 | ICML | Differentially Private Fair Learning. | Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman |
| 2019 | STOC | The structure of optimal private tests for simple hypotheses. | Clment L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman |
| 2018 | CRYPTO | Hardness of Non-interactive Differential Privacy from One-Way Functions. | Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Daniel Wichs |
| 2018 | ISIT | Skyline Identification in Multi-Arm Bandits. | Albert Cheu, Ravi Sundaram, Jonathan R. Ullman |
| 2017 | COLT | The Price of Selection in Differential Privacy. | Mitali Bafna, Jonathan R. Ullman |
| 2017 | FOCS | Tight Lower Bounds for Differentially Private Selection. | Thomas Steinke, Jonathan R. Ullman |
| 2017 | SODA | Make Up Your Mind: The Price of Online Queries in Differential Privacy. | Mark Bun, Thomas Steinke, Jonathan R. Ullman |
| 2016 | IDEAS | Computing Marginals Using MapReduce: Keynote talk paper. | Foto N. Afrati, Shantanu Sharma, Jeffrey D. Ullman, Jonathan R. Ullman |
| 2016 | ITA | Interactive fingerprinting codes and the hardness of preventing false discovery. | Thomas Steinke, Jonathan R. Ullman |
| 2016 | PODS | Space Lower Bounds for Itemset Frequency Sketches. | Edo Liberty, Michael Mitzenmacher, Justin Thaler, Jonathan R. Ullman |
| 2016 | SIGMOD | Some pairs problems. | Jeffrey D. Ullman, Jonathan R. Ullman |
| 2016 | STOC | Algorithmic stability for adaptive data analysis. | Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman |
| 2016 | STOC | Watch and learn: optimizing from revealed preferences feedback. | Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu |
| 2016 | TCC | Strong Hardness of Privacy from Weak Traitor Tracing. | Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Mark Zhandry |
| 2015 | COLT | Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery. | Thomas Steinke, Jonathan R. Ullman |
| 2015 | FOCS | Robust Traceability from Trace Amounts. | Cynthia Dwork, Adam D. Smith, Thomas Steinke, Jonathan R. Ullman, Salil P. Vadhan |
| 2015 | PODS | Private Multiplicative Weights Beyond Linear Queries. | Jonathan R. Ullman |
| 2015 | SAGT | When Can Limited Randomness Be Used in Repeated Games? | Pavel Hubcek, Moni Naor, Jonathan R. Ullman |
| 2014 | FOCS | Preventing False Discovery in Interactive Data Analysis Is Hard. | Moritz Hardt, Jonathan R. Ullman |
| 2014 | ICALP | Privately Solving Linear Programs. | Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan R. Ullman |
| 2014 | STOC | Fingerprinting codes and the price of approximate differential privacy. | Mark Bun, Jonathan R. Ullman, Salil P. Vadhan |
| 2013 | STOC | Differential privacy for the analyst via private equilibrium computation. | Justin Hsu, Aaron Roth, Jonathan R. Ullman |
| 2013 | STOC | Answering n | Jonathan R. Ullman |
| 2012 | ICALP | Faster Algorithms for Privately Releasing Marginals. | Justin Thaler, Jonathan R. Ullman, Salil P. Vadhan |
| 2012 | TCC | Iterative Constructions and Private Data Release. | Anupam Gupta, Aaron Roth, Jonathan R. Ullman |
| 2011 | ITA | On the zero-error capacity threshold for deletion channels. | Ian A. Kash, Michael Mitzenmacher, Justin Thaler, Jonathan R. Ullman |
| 2011 | STOC | Privately releasing conjunctions and the statistical query barrier. | Anupam Gupta, Moritz Hardt, Aaron Roth, Jonathan R. Ullman |
| 2011 | TCC | PCPs and the Hardness of Generating Private Synthetic Data. | Jonathan R. Ullman, Salil P. Vadhan |
| 2010 | STOC | The price of privately releasing contingency tables and the spectra of random matrices with correlated rows. | Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam D. Smith, Jonathan R. Ullman |