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Public-data Assisted Private Stochastic Optimization: Power and
  Limitations

Public-data Assisted Private Stochastic Optimization: Power and Limitations

6 March 2024
Enayat Ullah
Michael Menart
Raef Bassily
Cristóbal Guzmán
Raman Arora
ArXivPDFHTML

Papers citing "Public-data Assisted Private Stochastic Optimization: Power and Limitations"

28 / 28 papers shown
Title
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
60
12
0
11 Aug 2023
PILLAR: How to make semi-private learning more effective
PILLAR: How to make semi-private learning more effective
Francesco Pinto
Yaxian Hu
Fanny Yang
Amartya Sanyal
59
11
0
06 Jun 2023
On Size-Independent Sample Complexity of ReLU Networks
On Size-Independent Sample Complexity of ReLU Networks
Mark Sellke
27
6
0
03 Jun 2023
Why Is Public Pretraining Necessary for Private Model Training?
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
39
37
0
19 Feb 2023
Differentially Private Bias-Term Fine-tuning of Foundation Models
Differentially Private Bias-Term Fine-tuning of Foundation Models
Zhiqi Bu
Yu Wang
Sheng Zha
George Karypis
61
47
0
30 Sep 2022
Private Estimation with Public Data
Private Estimation with Public Data
Alex Bie
Gautam Kamath
Vikrant Singhal
44
30
0
16 Aug 2022
Differentially Private Generalized Linear Models Revisited
Differentially Private Generalized Linear Models Revisited
R. Arora
Raef Bassily
Cristóbal Guzmán
Michael Menart
Enayat Ullah
FedML
43
17
0
06 May 2022
Private Convex Optimization via Exponential Mechanism
Private Convex Optimization via Exponential Mechanism
Sivakanth Gopi
Y. Lee
Daogao Liu
100
54
0
01 Mar 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
41
50
0
01 Dec 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
177
356
0
13 Oct 2021
Differentially Private Stochastic Optimization: New Results in Convex
  and Non-Convex Settings
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings
Raef Bassily
Cristóbal Guzmán
Michael Menart
58
55
0
12 Jul 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for
  Private Learning
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
Da Yu
Huishuai Zhang
Wei Chen
Tie-Yan Liu
FedML
SILM
101
113
0
25 Feb 2021
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace
  Identification
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
Yingxue Zhou
Zhiwei Steven Wu
A. Banerjee
48
108
0
07 Jul 2020
A Primer on Private Statistics
A Primer on Private Statistics
Gautam Kamath
Jonathan R. Ullman
49
48
0
30 Apr 2020
Private Query Release Assisted by Public Data
Private Query Release Assisted by Public Data
Raef Bassily
Albert Cheu
Shay Moran
Aleksandar Nikolov
Jonathan R. Ullman
Zhiwei Steven Wu
104
48
0
23 Apr 2020
Limits of Private Learning with Access to Public Data
Limits of Private Learning with Access to Public Data
N. Alon
Raef Bassily
Shay Moran
21
48
0
25 Oct 2019
FedMD: Heterogenous Federated Learning via Model Distillation
FedMD: Heterogenous Federated Learning via Model Distillation
Daliang Li
Junpu Wang
FedML
75
845
0
08 Oct 2019
Private Stochastic Convex Optimization with Optimal Rates
Private Stochastic Convex Optimization with Optimal Rates
Raef Bassily
Vitaly Feldman
Kunal Talwar
Abhradeep Thakurta
48
240
0
27 Aug 2019
The Cost of Privacy: Optimal Rates of Convergence for Parameter
  Estimation with Differential Privacy
The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy
T. Tony Cai
Yichen Wang
Linjun Zhang
51
165
0
12 Feb 2019
Scalable Private Learning with PATE
Scalable Private Learning with PATE
Nicolas Papernot
Shuang Song
Ilya Mironov
A. Raghunathan
Kunal Talwar
Ulfar Erlingsson
83
608
0
24 Feb 2018
Size-Independent Sample Complexity of Neural Networks
Size-Independent Sample Complexity of Neural Networks
Noah Golowich
Alexander Rakhlin
Ohad Shamir
86
547
0
18 Dec 2017
Spectrally-normalized margin bounds for neural networks
Spectrally-normalized margin bounds for neural networks
Peter L. Bartlett
Dylan J. Foster
Matus Telgarsky
ODL
130
1,208
0
26 Jun 2017
Generalization for Adaptively-chosen Estimators via Stable Median
Generalization for Adaptively-chosen Estimators via Stable Median
Vitaly Feldman
Thomas Steinke
109
42
0
15 Jun 2017
Semi-supervised Knowledge Transfer for Deep Learning from Private
  Training Data
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
Nicolas Papernot
Martín Abadi
Ulfar Erlingsson
Ian Goodfellow
Kunal Talwar
52
1,012
0
18 Oct 2016
Statistical Query Algorithms for Mean Vector Estimation and Stochastic
  Convex Optimization
Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization
Vitaly Feldman
Cristóbal Guzmán
Santosh Vempala
61
89
0
30 Dec 2015
Between Pure and Approximate Differential Privacy
Between Pure and Approximate Differential Privacy
Thomas Steinke
Jonathan R. Ullman
FedML
52
159
0
24 Jan 2015
Private Learning and Sanitization: Pure vs. Approximate Differential
  Privacy
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
A. Beimel
Kobbi Nissim
Uri Stemmer
57
194
0
10 Jul 2014
Fingerprinting Codes and the Price of Approximate Differential Privacy
Fingerprinting Codes and the Price of Approximate Differential Privacy
Mark Bun
Jonathan R. Ullman
Salil P. Vadhan
FedML
45
211
0
13 Nov 2013
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