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Bounding the Excess Risk for Linear Models Trained on
  Marginal-Preserving, Differentially-Private, Synthetic Data
v1v2 (latest)

Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data

6 February 2024
Yvonne Zhou
Mingyu Liang
Ivan Brugere
Dana Dachman-Soled
Danial Dervovic
Antigoni Polychroniadou
Min Wu
ArXiv (abs)PDFHTML

Papers citing "Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data"

13 / 13 papers shown
Title
Statistical Theory of Differentially Private Marginal-based Data
  Synthesis Algorithms
Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms
Ximing Li
Chendi Wang
Guang Cheng
SyDa
150
8
0
21 Jan 2023
Variational Model Inversion Attacks
Variational Model Inversion Attacks
Kuan-Chieh Wang
Yanzhe Fu
Ke Li
Ashish Khisti
R. Zemel
Alireza Makhzani
MIACV
76
97
0
26 Jan 2022
Hyperparameter Tuning with Renyi Differential Privacy
Hyperparameter Tuning with Renyi Differential Privacy
Nicolas Papernot
Thomas Steinke
184
129
0
07 Oct 2021
Winning the NIST Contest: A scalable and general approach to
  differentially private synthetic data
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data
Ryan McKenna
G. Miklau
Daniel Sheldon
SyDa
59
126
0
11 Aug 2021
An Efficient DP-SGD Mechanism for Large Scale NLP Models
An Efficient DP-SGD Mechanism for Large Scale NLP Models
Christophe Dupuy
Radhika Arava
Rahul Gupta
Anna Rumshisky
SyDa
72
36
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
67
64
0
14 Jun 2021
Dopamine: Differentially Private Federated Learning on Medical Data
Dopamine: Differentially Private Federated Learning on Medical Data
Mohammad Malekzadeh
Burak Hasircioglu
N. Mital
K. Katarya
M. E. Ozfatura
Deniz Gündüz
OODFedML
87
51
0
27 Jan 2021
How to Democratise and Protect AI: Fair and Differentially Private
  Decentralised Deep Learning
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
Lingjuan Lyu
Yitong Li
Karthik Nandakumar
Jiangshan Yu
Xingjun Ma
FedML
50
52
0
18 Jul 2020
Gradient Perturbation is Underrated for Differentially Private Convex
  Optimization
Gradient Perturbation is Underrated for Differentially Private Convex Optimization
Da Yu
Huishuai Zhang
Kwei-Herng Lai
Yuening Li
Helen Zhou
61
37
0
26 Nov 2019
Graphical-model based estimation and inference for differential privacy
Graphical-model based estimation and inference for differential privacy
Ryan McKenna
Daniel Sheldon
G. Miklau
63
144
0
26 Jan 2019
Real-valued (Medical) Time Series Generation with Recurrent Conditional
  GANs
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
Cristóbal Esteban
Stephanie L. Hyland
Gunnar Rätsch
GANSyDaMedIm
112
792
0
08 Jun 2017
Membership Inference Attacks against Machine Learning Models
Membership Inference Attacks against Machine Learning Models
Reza Shokri
M. Stronati
Congzheng Song
Vitaly Shmatikov
SLRMIALMMIACV
278
4,160
0
18 Oct 2016
Deep Learning with Differential Privacy
Deep Learning with Differential Privacy
Martín Abadi
Andy Chu
Ian Goodfellow
H. B. McMahan
Ilya Mironov
Kunal Talwar
Li Zhang
FedMLSyDa
216
6,172
0
01 Jul 2016
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