Private Estimation with Public Data

16 August 2022

Papers citing "Private Estimation with Public Data"

26 / 26 papers shown

Title
Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation Samuel Maddock Shripad Gade Graham Cormode Will Bullock 26 0 0 15 Apr 2025
Optimal Differentially Private Sampling of Unbounded Gaussians Valentio Iverson Gautam Kamath Argyris Mouzakis 44 0 0 03 Mar 2025
Differentially Private Prototypes for Imbalanced Transfer Learning Dariush Wahdany Matthew Jagielski Adam Dziedzic Franziska Boenisch 80 0 0 17 Feb 2025
Private Means and the Curious Incident of the Free Lunch Jack Fitzsimons James Honaker Michael Shoemate Vikrant Singhal 27 2 0 19 Aug 2024
Credit Attribution and Stable Compression Roi Livni Shay Moran Kobbi Nissim Chirag Pabbaraju 34 0 0 22 Jun 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations Enayat Ullah Michael Menart Raef Bassily Cristóbal Guzmán Raman Arora 30 1 0 06 Mar 2024
On the Convergence of Differentially-Private Fine-tuning: To Linearly Probe or to Fully Fine-tune? Shuqi Ke Charlie Hou Giulia Fanti Sewoong Oh 34 4 0 29 Feb 2024
Oracle-Efficient Differentially Private Learning with Public Data Adam Block Mark Bun Rathin Desai Abhishek Shetty Steven Wu FedML 11 2 0 13 Feb 2024
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity A. F. Pour Hassan Ashtiani S. Asoodeh 18 0 0 09 Dec 2023
Mean Estimation Under Heterogeneous Privacy Demands Syomantak Chaudhuri Konstantin Miagkov T. Courtade 10 1 0 19 Oct 2023
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples Mohammad Afzali H. Ashtiani Christopher Liaw 19 5 0 07 Sep 2023
Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David Alex Bie C. Canonne Gautam Kamath Vikrant Singhal 22 11 0 11 Aug 2023
Differentially Private Domain Adaptation with Theoretical Guarantees Raef Bassily Corinna Cortes Anqi Mao M. Mohri 17 0 0 15 Jun 2023
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zi-Han Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 17 19 0 23 May 2023
Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free Syomantak Chaudhuri T. Courtade 11 4 0 27 Apr 2023
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions Vikrant Singhal 13 9 0 13 Apr 2023
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models Jamil Arbas H. Ashtiani Christopher Liaw 32 23 0 07 Mar 2023
Why Is Public Pretraining Necessary for Private Model Training? Arun Ganesh Mahdi Haghifam Milad Nasr Sewoong Oh Thomas Steinke Om Thakkar Abhradeep Thakurta Lun Wang 11 36 0 19 Feb 2023
Learning-Augmented Private Algorithms for Multiple Quantile Release M. Khodak Kareem Amin Travis Dick Sergei Vassilvitskii FedML 16 4 0 20 Oct 2022
Models of fairness in federated learning Kate Donahue Jon M. Kleinberg FedML 23 9 0 01 Dec 2021
A Private and Computationally-Efficient Estimator for Unbounded Gaussians Gautam Kamath Argyris Mouzakis Vikrant Singhal Thomas Steinke Jonathan R. Ullman 50 39 0 08 Nov 2021
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 134 344 0 13 Oct 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 91 110 0 25 Feb 2021
Leveraging Public Data for Practical Private Query Release Terrance Liu G. Vietri Thomas Steinke Jonathan R. Ullman Zhiwei Steven Wu 148 58 0 17 Feb 2021
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians Ishaq Aden-Ali H. Ashtiani Gautam Kamath 32 41 0 19 Oct 2020
Privately Learning High-Dimensional Distributions Gautam Kamath Jerry Li Vikrant Singhal Jonathan R. Ullman FedML 62 147 0 01 May 2018