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Publication search results
found 160 matches
- 2024
- Jiawen Liu, Weihao Qu, Marco Gaboardi, Deepak Garg, Jonathan R. Ullman:
Program Analysis for Adaptive Data Analysis. Proc. ACM Program. Lang. 8(PLDI): 914-938 (2024) - John Abascal, Stanley Wu, Alina Oprea, Jonathan R. Ullman:
TMI! Finetuned Models Leak Private Information from their Pretraining Data. Proc. Priv. Enhancing Technol. 2024(3): 202-223 (2024) - Liudas Panavas, Tarik Crnovrsanin, Jane Lydia Adams, Jonathan Ullman, Ali Sargavad, Melanie Tory, Cody Dunne:
Investigating the Visual Utility of Differentially Private Scatterplots. IEEE Trans. Vis. Comput. Graph. 30(8): 5370-5385 (2024) - Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman:
Metalearning with Very Few Samples Per Task. COLT 2024: 46-93 - Naty Peter, Eliad Tsfadia, Jonathan R. Ullman:
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes. COLT 2024: 4207-4239 - Harsh Chaudhari, Giorgio Severi, Alina Oprea, Jonathan R. Ullman:
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning. ICLR 2024 - Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright:
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. ICML 2024 - Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman:
Differentially Private Medians and Interior Points for Non-Pathological Data. ITCS 2024: 3:1-3:21 - Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright:
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. CoRR abs/2402.11173 (2024) - Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, Jonathan R. Ullman:
Private Mean Estimation with Person-Level Differential Privacy. CoRR abs/2405.20405 (2024) - Mahdi Haghifam, Thomas Steinke, Jonathan R. Ullman:
Private Geometric Median. CoRR abs/2406.07407 (2024) - 2023
- Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu:
How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models. Proc. Priv. Enhancing Technol. 2023(3): 211-232 (2023) - Konstantina Bairaktari, Paul Langton, Huy L. Nguyen, Niklas Smedemark-Margulies, Jonathan R. Ullman:
Fair and Useful Cohort Selection. Trans. Mach. Learn. Res. 2023 (2023) - Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou:
Multitask Learning via Shared Features: Algorithms and Hardness. COLT 2023: 747-772 - Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou:
From Robustness to Privacy and Back. ICML 2023: 1121-1146 - Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman:
SNAP: Efficient Extraction of Private Properties with Poisoning. SP 2023: 400-417 - Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman:
A Bias-Variance-Privacy Trilemma for Statistical Estimation. CoRR abs/2301.13334 (2023) - Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou:
From Robustness to Privacy and Back. CoRR abs/2302.01855 (2023) - Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman:
Differentially Private Medians and Interior Points for Non-Pathological Data. CoRR abs/2305.13440 (2023) - John Abascal, Stanley Wu, Alina Oprea, Jonathan R. Ullman:
TMI! Finetuned Models Leak Private Information from their Pretraining Data. CoRR abs/2306.01181 (2023) - Naty Peter, Eliad Tsfadia, Jonathan R. Ullman:
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes. CoRR abs/2307.07604 (2023) - Harsh Chaudhari, Giorgio Severi, Alina Oprea, Jonathan R. Ullman:
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning. CoRR abs/2310.03838 (2023) - Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Jonathan R. Ullman:
Metalearning with Very Few Samples Per Task. CoRR abs/2312.13978 (2023) - 2022
- Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman:
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. COLT 2022: 544-572 - Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu:
How to Combine Membership-Inference Attacks on Multiple Updated Models. CoRR abs/2205.06369 (2022) - Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman:
SNAP: Efficient Extraction of Private Properties with Poisoning. CoRR abs/2208.12348 (2022) - Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou:
Multitask Learning via Shared Features: Algorithms and Hardness. CoRR abs/2209.03112 (2022) - Audra McMillan, Adam D. Smith, Jonathan R. Ullman:
Instance-Optimal Differentially Private Estimation. CoRR abs/2210.15819 (2022) - 2021
- Albert Cheu, Adam D. Smith, Jonathan R. Ullman:
Manipulation Attacks in Local Differential Privacy. J. Priv. Confidentiality 11(1) (2021) - Adam Sealfon, Jonathan R. Ullman:
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. J. Priv. Confidentiality 11(1) (2021)
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