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2004.10941
Cited By
Private Query Release Assisted by Public Data
23 April 2020
Raef Bassily
Albert Cheu
Shay Moran
Aleksandar Nikolov
Jonathan R. Ullman
Zhiwei Steven Wu
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Papers citing
"Private Query Release Assisted by Public Data"
32 / 32 papers shown
Managing Correlations in Data and Privacy Demand
Syomantak Chaudhuri
T. Courtade
160
0
0
02 Sep 2025
Lower Bounds for Public-Private Learning under Distribution Shift
Amrith Rajagopal Setlur
Pratiksha Thaker
Jonathan Ullman
FedML
210
0
0
23 Jul 2025
Credit Attribution and Stable Compression
Roi Livni
Shay Moran
Kobbi Nissim
Chirag Pabbaraju
255
2
0
22 Jun 2024
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Miguel Fuentes
Brett Mullins
Ryan McKenna
G. Miklau
Daniel Sheldon
226
11
0
12 Mar 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations
Enayat Ullah
Michael Menart
Raef Bassily
Cristóbal Guzmán
Raman Arora
347
3
0
06 Mar 2024
Oracle-Efficient Differentially Private Learning with Public Data
Adam Block
Mark Bun
Rathin Desai
Abhishek Shetty
Steven Wu
FedML
364
2
0
13 Feb 2024
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift
Pratiksha Thaker
Amrith Rajagopal Setlur
Zhiwei Steven Wu
Virginia Smith
468
7
0
24 Dec 2023
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance
Haichao Sha
Ruixuan Liu
Yi-xiao Liu
Hong Chen
333
2
0
06 Dec 2023
Mean Estimation Under Heterogeneous Privacy Demands
Syomantak Chaudhuri
Konstantin Miagkov
T. Courtade
302
5
0
19 Oct 2023
Private Distribution Learning with Public Data: The View from Sample Compression
Neural Information Processing Systems (NeurIPS), 2023
Shai Ben-David
Alex Bie
C. Canonne
Gautam Kamath
Vikrant Singhal
354
17
0
11 Aug 2023
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes
Annual Conference Computational Learning Theory (COLT), 2023
Naty Peter
Eliad Tsfadia
Jonathan R. Ullman
503
8
0
14 Jul 2023
Differentially Private Domain Adaptation with Theoretical Guarantees
International Conference on Machine Learning (ICML), 2023
Raef Bassily
Corinna Cortes
Anqi Mao
M. Mohri
295
0
0
15 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
448
26
0
23 May 2023
Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free
International Symposium on Information Theory (ISIT), 2023
Syomantak Chaudhuri
T. Courtade
339
6
0
27 Apr 2023
Can Membership Inferencing be Refuted?
Zhifeng Kong
A. Chowdhury
Kamalika Chaudhuri
MIALM
MIACV
414
8
0
07 Mar 2023
Why Is Public Pretraining Necessary for Private Model Training?
International Conference on Machine Learning (ICML), 2023
Arun Ganesh
Mahdi Haghifam
Milad Nasr
Sewoong Oh
Thomas Steinke
Om Thakkar
Abhradeep Thakurta
Lun Wang
204
46
0
19 Feb 2023
Pushing the Boundaries of Private, Large-Scale Query Answering
Brendan Avent
Aleksandra Korolova
205
0
0
09 Feb 2023
Private Estimation with Public Data
Neural Information Processing Systems (NeurIPS), 2022
Alex Bie
Gautam Kamath
Vikrant Singhal
336
36
0
16 Aug 2022
Private Domain Adaptation from a Public Source
Raef Bassily
M. Mohri
A. Suresh
171
4
0
12 Aug 2022
Formal Privacy for Partially Private Data
Jeremy Seeman
M. Reimherr
Aleksandra B. Slavkovic
300
3
0
03 Apr 2022
EIFFeL: Ensuring Integrity for Federated Learning
Conference on Computer and Communications Security (CCS), 2021
A. Chowdhury
Chuan Guo
S. Jha
Laurens van der Maaten
FedML
435
110
0
23 Dec 2021
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
379
61
0
01 Dec 2021
Realizable Learning is All You Need
Max Hopkins
D. Kane
Shachar Lovett
G. Mahajan
603
27
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
713
477
0
13 Oct 2021
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods
Neural Information Processing Systems (NeurIPS), 2021
Terrance Liu
G. Vietri
Zhiwei Steven Wu
SyDa
282
76
0
14 Jun 2021
Privately Learning Subspaces
Neural Information Processing Systems (NeurIPS), 2021
Vikrant Singhal
Thomas Steinke
595
22
0
28 May 2021
Leveraging Public Data for Practical Private Query Release
International Conference on Machine Learning (ICML), 2021
Terrance Liu
G. Vietri
Thomas Steinke
Jonathan R. Ullman
Zhiwei Steven Wu
459
68
0
17 Feb 2021
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
International Conference on Learning Representations (ICLR), 2020
Hong-You Chen
Wei-Lun Chao
FedML
426
325
0
04 Sep 2020
Learning from Mixtures of Private and Public Populations
Neural Information Processing Systems (NeurIPS), 2020
Raef Bassily
Shay Moran
Anupama Nandi
FedML
140
28
0
01 Aug 2020
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
International Conference on Learning Representations (ICLR), 2020
Yingxue Zhou
Zhiwei Steven Wu
A. Banerjee
316
117
0
07 Jul 2020
PAC learning with stable and private predictions
Annual Conference Computational Learning Theory (COLT), 2019
Y. Dagan
Vitaly Feldman
344
16
0
24 Nov 2019
The Power of The Hybrid Model for Mean Estimation
Brendan Avent
Yatharth Dubey
Aleksandra Korolova
412
18
0
29 Nov 2018
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