Limits of Private Learning with Access to Public Data

25 October 2019

Papers citing "Limits of Private Learning with Access to Public Data"

36 / 36 papers shown

Title
Credit Attribution and Stable Compression Roi Livni Shay Moran Kobbi Nissim Chirag Pabbaraju 72 0 0 22 Jun 2024
Fast Rates for Bandit PAC Multiclass Classification Liad Erez Alon Cohen Tomer Koren Yishay Mansour Shay Moran 45 1 0 18 Jun 2024
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data Miguel Fuentes Brett Mullins Ryan McKenna G. Miklau Daniel Sheldon 73 5 0 12 Mar 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations Enayat Ullah Michael Menart Raef Bassily Cristóbal Guzmán Raman Arora 74 2 0 06 Mar 2024
Oracle-Efficient Differentially Private Learning with Public Data Adam Block Mark Bun Rathin Desai Abhishek Shetty Steven Wu FedML 53 2 0 13 Feb 2024
The sample complexity of multi-distribution learning Binghui Peng 117 9 0 07 Dec 2023
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance Haichao Sha Ruixuan Liu Yi-xiao Liu Hong Chen 133 1 0 06 Dec 2023
Mean Estimation Under Heterogeneous Privacy Demands Syomantak Chaudhuri Konstantin Miagkov T. Courtade 77 1 0 19 Oct 2023
Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David Alex Bie C. Canonne Gautam Kamath Vikrant Singhal 86 13 0 11 Aug 2023
Differentially Private Domain Adaptation with Theoretical Guarantees Raef Bassily Corinna Cortes Anqi Mao M. Mohri 60 0 0 15 Jun 2023
PILLAR: How to make semi-private learning more effective Francesco Pinto Yaxian Hu Fanny Yang Amartya Sanyal 92 12 0 06 Jun 2023
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zinan Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 94 19 0 23 May 2023
Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free Syomantak Chaudhuri T. Courtade 47 4 0 27 Apr 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 68 39 0 19 Feb 2023
Do PAC-Learners Learn the Marginal Distribution? Max Hopkins D. Kane Shachar Lovett G. Mahajan 200 3 0 13 Feb 2023
Pushing the Boundaries of Private, Large-Scale Query Answering Brendan Avent Aleksandra Korolova 78 0 0 09 Feb 2023
Differentially-Private Bayes Consistency Olivier Bousquet Haim Kaplan A. Kontorovich Yishay Mansour Shay Moran Menachem Sadigurschi Uri Stemmer 51 0 0 08 Dec 2022
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling Junyuan Hong Lingjuan Lyu Jiayu Zhou Michael Spranger SyDa 86 6 0 23 Oct 2022
Private Estimation with Public Data Alex Bie Gautam Kamath Vikrant Singhal 78 31 0 16 Aug 2022
Private Domain Adaptation from a Public Source Raef Bassily M. Mohri A. Suresh 20 4 0 12 Aug 2022
Mixed Differential Privacy in Computer Vision Aditya Golatkar Alessandro Achille Yu Wang Aaron Roth Michael Kearns Stefano Soatto PICV VLM 96 50 0 22 Mar 2022
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy Rui Hu Yanmin Gong Yuanxiong Guo FedML 87 73 0 15 Feb 2022
Public Data-Assisted Mirror Descent for Private Model Training Ehsan Amid Arun Ganesh Rajiv Mathews Swaroop Indra Ramaswamy Shuang Song Thomas Steinke Vinith Suriyakumar Om Thakkar Abhradeep Thakurta 89 51 0 01 Dec 2021
Realizable Learning is All You Need Max Hopkins D. Kane Shachar Lovett G. Mahajan 173 23 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 260 372 0 13 Oct 2021
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods Terrance Liu G. Vietri Zhiwei Steven Wu SyDa 70 64 0 14 Jun 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 169 116 0 25 Feb 2021
Leveraging Public Data for Practical Private Query Release Terrance Liu G. Vietri Thomas Steinke Jonathan R. Ullman Zhiwei Steven Wu 198 60 0 17 Feb 2021
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning Chong Liu Yuqing Zhu Kamalika Chaudhuri Yu Wang FedML 66 8 0 06 Nov 2020
Learning from Mixtures of Private and Public Populations Raef Bassily Shay Moran Anupama Nandi FedML 50 25 0 01 Aug 2020
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification Yingxue Zhou Zhiwei Steven Wu A. Banerjee 74 110 0 07 Jul 2020
Private Query Release Assisted by Public Data Raef Bassily Albert Cheu Shay Moran Aleksandar Nikolov Jonathan R. Ullman Zhiwei Steven Wu 132 49 0 23 Apr 2020
Reasoning About Generalization via Conditional Mutual Information Thomas Steinke Lydia Zakynthinou 161 166 0 24 Jan 2020
PAC learning with stable and private predictions Y. Dagan Vitaly Feldman 67 13 0 24 Nov 2019
Privately Answering Classification Queries in the Agnostic PAC Model Anupama Nandi Raef Bassily 104 26 0 31 Jul 2019
The Power of The Hybrid Model for Mean Estimation Brendan Avent Yatharth Dubey Aleksandra Korolova 79 17 0 29 Nov 2018