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1502.06309
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Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
23 February 2015
Yu Wang
Jing Lei
S. Fienberg
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Papers citing
"Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle"
44 / 44 papers shown
Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning
IACR Cryptology ePrint Archive (IACR ePrint), 2025
Dustin Ray
Caroline El Jazmi
255
1
0
13 Jun 2025
On the ERM Principle in Meta-Learning
Yannay Alon
Steve Hanneke
Shay Moran
Uri Shalit
CLL
LRM
435
2
0
26 Nov 2024
The Complexities of Differential Privacy for Survey Data
Social Science Research Network (SSRN), 2024
Jorg Drechsler
James Bailie
348
6
0
13 Aug 2024
Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature
Deepak Ravikumar
Efstathia Soufleri
Kaushik Roy
243
5
0
03 Jul 2024
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning
International Conference on Machine Learning (ICML), 2024
Chendi Wang
Yuqing Zhu
Weijie J. Su
Yu Wang
AAML
293
8
0
14 May 2024
Unveiling Privacy, Memorization, and Input Curvature Links
Deepak Ravikumar
Efstathia Soufleri
Abolfazl Hashemi
Kaushik Roy
346
15
0
28 Feb 2024
Stability and L2-penalty in Model Averaging
Hengkun Zhu
Guohua Zou
MoMe
178
2
0
23 Nov 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis
Conference on Computer and Communications Security (CCS), 2023
Kunal Talwar
Shan Wang
Audra McMillan
Vojta Jina
Vitaly Feldman
...
Congzheng Song
Karl Tarbe
Sebastian Vogt
L. Winstrom
Shundong Zhou
FedML
503
20
0
27 Jul 2023
A unifying framework for differentially private quantum algorithms
Armando Angrisani
Mina Doosti
E. Kashefi
FedML
335
16
0
10 Jul 2023
On the Privacy Properties of GAN-generated Samples
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Zinan Lin
Vyas Sekar
Giulia Fanti
PICV
234
38
0
03 Jun 2022
What You See is What You Get: Principled Deep Learning via Distributional Generalization
Neural Information Processing Systems (NeurIPS), 2022
B. Kulynych
Yao-Yuan Yang
Yaodong Yu
Jarosław Błasiok
Preetum Nakkiran
OOD
351
11
0
07 Apr 2022
Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms
Armando Angrisani
Mina Doosti
E. Kashefi
334
15
0
07 Mar 2022
Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis
Bingzhe Wu
Zhicong Liang
Yatao Bian
Chaochao Chen
Junzhou Huang
Yuan Yao
155
1
0
14 Dec 2021
Time-independent Generalization Bounds for SGLD in Non-convex Settings
Tyler Farghly
Patrick Rebeschini
256
29
0
25 Nov 2021
Selective Ensembles for Consistent Predictions
International Conference on Learning Representations (ICLR), 2021
Emily Black
Klas Leino
Matt Fredrikson
202
28
0
16 Nov 2021
Accuracy Gains from Privacy Amplification Through Sampling for Differential Privacy
Jingchen Hu
Joerg Drechsler
Hang J Kim
FedML
312
3
0
17 Mar 2021
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
Chong Liu
Yuqing Zhu
Kamalika Chaudhuri
Yu Wang
FedML
320
9
0
06 Nov 2020
Controlling Privacy Loss in Sampling Schemes: an Analysis of Stratified and Cluster Sampling
Symposium on Foundations of Responsible Computing (FRC), 2020
Mark Bun
Jörg Drechsler
Marco Gaboardi
Audra McMillan
Jayshree Sarathy
341
7
0
24 Jul 2020
Stability Enhanced Privacy and Applications in Private Stochastic Gradient Descent
Lauren Watson
Benedek Rozemberczki
Rik Sarkar
213
1
0
25 Jun 2020
Upper Bounds on the Generalization Error of Private Algorithms for Discrete Data
Borja Rodríguez Gálvez
Germán Bassi
Mikael Skoglund
322
9
0
12 May 2020
Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?
R. Jia
Fan Wu
Xuehui Sun
Jiacen Xu
David Dao
Bhavya Kailkhura
Ce Zhang
Yue Liu
Basel Alomair
TDI
201
21
0
17 Nov 2019
Fault Tolerance of Neural Networks in Adversarial Settings
Journal of Intelligent & Fuzzy Systems (JIFS), 2019
Vasisht Duddu
N. Pillai
D. V. Rao
V. Balas
SILM
AAML
235
12
0
30 Oct 2019
Sharper bounds for uniformly stable algorithms
Annual Conference Computational Learning Theory (COLT), 2019
Olivier Bousquet
Yegor Klochkov
Nikita Zhivotovskiy
304
140
0
17 Oct 2019
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
Neural Information Processing Systems (NeurIPS), 2019
Bingzhe Wu
Shiwan Zhao
Chaochao Chen
Haoyang Xu
Li Wang
Xiaolu Zhang
Guangyu Sun
Jun Zhou
206
45
0
21 Aug 2019
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM
Mathematical and Scientific Machine Learning (MSML), 2019
Bao Wang
Quanquan Gu
M. Boedihardjo
Farzin Barekat
Stanley J. Osher
403
31
0
28 Jun 2019
A New Approach to Adaptive Data Analysis and Learning via Maximal Leakage
A. Esposito
Michael C. Gastpar
Ibrahim Issa
247
7
0
05 Mar 2019
Stable and Fair Classification
Lingxiao Huang
Nisheeth K. Vishnoi
FaML
578
73
0
21 Feb 2019
Privacy-preserving Stochastic Gradual Learning
Bo Han
Ivor W. Tsang
Xiaokui Xiao
Ling-Hao Chen
S. Fung
C. Yu
NoLa
164
9
0
30 Sep 2018
Subsampled Rényi Differential Privacy and Analytical Moments Accountant
Yu Wang
Borja Balle
S. Kasiviswanathan
509
463
0
31 Jul 2018
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Neural Information Processing Systems (NeurIPS), 2018
Borja Balle
Gilles Barthe
Marco Gaboardi
341
467
0
04 Jul 2018
Private PAC learning implies finite Littlestone dimension
N. Alon
Roi Livni
M. Malliaris
Shay Moran
304
128
0
04 Jun 2018
Learning Anonymized Representations with Adversarial Neural Networks
Clément Feutry
Pablo Piantanida
Yoshua Bengio
Pierre Duhamel
258
61
0
26 Feb 2018
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Haiyan Zhao
Minwei Ye
Jinhui Xu
403
292
0
14 Feb 2018
On Connecting Stochastic Gradient MCMC and Differential Privacy
International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
Bai Li
Changyou Chen
Hao Liu
Lawrence Carin
224
40
0
25 Dec 2017
Online Learning via the Differential Privacy Lens
Jacob D. Abernethy
Young Hun Jung
Chansoo Lee
Audra McMillan
Ambuj Tewari
341
14
0
27 Nov 2017
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent
Ben London
341
82
0
19 Sep 2017
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Samuel Yeom
Irene Giacomelli
Matt Fredrikson
S. Jha
MIACV
494
41
0
05 Sep 2017
Fast Exact Conformalization of Lasso using Piecewise Linear Homotopy
Jing Lei
357
41
0
01 Aug 2017
Variational Bayes In Private Settings (VIPS)
Mijung Park
James R. Foulds
Kamalika Chaudhuri
Max Welling
637
42
0
01 Nov 2016
Efficient differentially private learning improves drug sensitivity prediction
Biology Direct (Biol Direct), 2016
Antti Honkela
Mrinal Das
Arttu Nieminen
O. Dikmen
Samuel Kaski
OOD
243
21
0
07 Jun 2016
On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms
Yu Wang
Jing Lei
S. Fienberg
247
50
0
08 May 2016
Generalization in Adaptive Data Analysis and Holdout Reuse
Neural Information Processing Systems (NeurIPS), 2015
Cynthia Dwork
Vitaly Feldman
Moritz Hardt
T. Pitassi
Omer Reingold
Aaron Roth
302
250
0
08 Jun 2015
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Yu Wang
S. Fienberg
Alex Smola
330
254
0
26 Feb 2015
Preserving Statistical Validity in Adaptive Data Analysis
Symposium on the Theory of Computing (STOC), 2014
Cynthia Dwork
Vitaly Feldman
Moritz Hardt
T. Pitassi
Omer Reingold
Aaron Roth
424
404
0
10 Nov 2014
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