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Learning from Mixtures of Private and Public Populations

Learning from Mixtures of Private and Public Populations

1 August 2020
Raef Bassily
Shay Moran
Anupama Nandi
    FedML
ArXiv (abs)PDFHTML

Papers citing "Learning from Mixtures of Private and Public Populations"

16 / 16 papers shown
Title
Oracle-Efficient Differentially Private Learning with Public Data
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
Private Learning with Public Features
Private Learning with Public Features
Walid Krichene
Nicolas Mayoraz
Steffen Rendle
Shuang Song
Abhradeep Thakurta
Li Zhang
70
8
0
24 Oct 2023
Mean Estimation Under Heterogeneous Privacy Demands
Mean Estimation Under Heterogeneous Privacy Demands
Syomantak Chaudhuri
Konstantin Miagkov
T. Courtade
77
1
0
19 Oct 2023
Private Matrix Factorization with Public Item Features
Private Matrix Factorization with Public Item Features
Mihaela Curmei
Walid Krichene
Li Zhang
Mukund Sundararajan
77
3
0
17 Sep 2023
Private Distribution Learning with Public Data: The View from Sample
  Compression
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 Heavy Hitter Detection using Federated Analytics
Differentially Private Heavy Hitter Detection using Federated Analytics
Karan N. Chadha
Junye Chen
John C. Duchi
Vitaly Feldman
H. Hashemi
O. Javidbakht
Audra McMillan
Kunal Talwar
FedML
64
10
0
21 Jul 2023
Selective Pre-training for Private Fine-tuning
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
Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free
Syomantak Chaudhuri
T. Courtade
47
4
0
27 Apr 2023
Private Estimation with Public Data
Private Estimation with Public Data
Alex Bie
Gautam Kamath
Vikrant Singhal
78
31
0
16 Aug 2022
Public Data-Assisted Mirror Descent for Private Model Training
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
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
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
A Field Guide to Federated Optimization
A Field Guide to Federated Optimization
Jianyu Wang
Zachary B. Charles
Zheng Xu
Gauri Joshi
H. B. McMahan
...
Mi Zhang
Tong Zhang
Chunxiang Zheng
Chen Zhu
Wennan Zhu
FedML
277
422
0
14 Jul 2021
Iterative Methods for Private Synthetic Data: Unifying Framework and New
  Methods
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
Leveraging Public Data for Practical Private Query Release
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
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
Hong-You Chen
Wei-Lun Chao
FedML
98
262
0
04 Sep 2020
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