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Training Private Models That Know What They Don't Know

Training Private Models That Know What They Don't Know

28 May 2023
Stephan Rabanser
Anvith Thudi
Abhradeep Thakurta
Krishnamurthy Dvijotham
Nicolas Papernot
ArXiv (abs)PDFHTML

Papers citing "Training Private Models That Know What They Don't Know"

26 / 26 papers shown
Title
Recycling Scraps: Improving Private Learning by Leveraging Intermediate
  Checkpoints
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints
Virat Shejwalkar
Arun Ganesh
Rajiv Mathews
Om Thakkar
Abhradeep Thakurta
72
0
0
04 Oct 2022
Towards Better Selective Classification
Towards Better Selective Classification
Leo Feng
Mohamed Osama Ahmed
Hossein Hajimirsadeghi
A. Abdi
65
23
0
17 Jun 2022
Selective Classification Via Neural Network Training Dynamics
Selective Classification Via Neural Network Training Dynamics
Stephan Rabanser
Anvith Thudi
Kimia Hamidieh
Adam Dziedzic
Nicolas Papernot
96
22
0
26 May 2022
Unbiased Statistical Estimation and Valid Confidence Intervals Under
  Differential Privacy
Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy
Christian Covington
Xi He
James Honaker
Gautam Kamath
88
26
0
27 Oct 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch
Opacus: User-Friendly Differential Privacy Library in PyTorch
Ashkan Yousefpour
I. Shilov
Alexandre Sablayrolles
Davide Testuggine
Karthik Prasad
...
Sayan Gosh
Akash Bharadwaj
Jessica Zhao
Graham Cormode
Ilya Mironov
VLM
291
368
0
25 Sep 2021
Generating and Characterizing Scenarios for Safety Testing of Autonomous
  Vehicles
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Zahra Ghodsi
S. Hari
I. Frosio
Timothy Tsai
Alejandro J. Troccoli
S. Keckler
S. Garg
Anima Anandkumar
79
43
0
12 Mar 2021
Selective Classification Can Magnify Disparities Across Groups
Selective Classification Can Magnify Disparities Across Groups
Erik Jones
Shiori Sagawa
Pang Wei Koh
Ananya Kumar
Percy Liang
97
47
0
27 Oct 2020
Regression with reject option and application to kNN
Regression with reject option and application to kNN
Christophe Denis
Mohamed Hebiri
A. Zaoui
63
36
0
30 Jun 2020
Simple and Principled Uncertainty Estimation with Deterministic Deep
  Learning via Distance Awareness
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Zhe Liu
Zi Lin
Shreyas Padhy
Dustin Tran
Tania Bedrax-Weiss
Balaji Lakshminarayanan
UQCVBDL
246
451
0
17 Jun 2020
Parametric Bootstrap for Differentially Private Confidence Intervals
Parametric Bootstrap for Differentially Private Confidence Intervals
Cecilia Ferrando
Shufan Wang
Daniel Sheldon
58
40
0
14 Jun 2020
Consistent Estimators for Learning to Defer to an Expert
Consistent Estimators for Learning to Defer to an Expert
Hussein Mozannar
David Sontag
61
205
0
02 Jun 2020
Self-Adaptive Training: beyond Empirical Risk Minimization
Self-Adaptive Training: beyond Empirical Risk Minimization
Lang Huang
Chaoning Zhang
Hongyang R. Zhang
NoLa
88
205
0
24 Feb 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
565
42,639
0
03 Dec 2019
Deep Gamblers: Learning to Abstain with Portfolio Theory
Deep Gamblers: Learning to Abstain with Portfolio Theory
Liu Ziyin
Zhikang T. Wang
Paul Pu Liang
Ruslan Salakhutdinov
Louis-Philippe Morency
Masahito Ueda
96
113
0
29 Jun 2019
Differential Privacy Has Disparate Impact on Model Accuracy
Differential Privacy Has Disparate Impact on Model Accuracy
Eugene Bagdasaryan
Vitaly Shmatikov
153
482
0
28 May 2019
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
Yonatan Geifman
Ran El-Yaniv
CVBMOOD
131
311
0
26 Jan 2019
Finite Sample Differentially Private Confidence Intervals
Finite Sample Differentially Private Confidence Intervals
Vishesh Karwa
Salil P. Vadhan
66
195
0
10 Nov 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
  Algorithms
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao
Kashif Rasul
Roland Vollgraf
285
8,928
0
25 Aug 2017
On Calibration of Modern Neural Networks
On Calibration of Modern Neural Networks
Chuan Guo
Geoff Pleiss
Yu Sun
Kilian Q. Weinberger
UQCV
299
5,877
0
14 Jun 2017
Selective Classification for Deep Neural Networks
Selective Classification for Deep Neural Networks
Yonatan Geifman
Ran El-Yaniv
CVBM
101
530
0
23 May 2017
Renyi Differential Privacy
Renyi Differential Privacy
Ilya Mironov
91
1,268
0
24 Feb 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCVBDL
847
5,847
0
05 Dec 2016
A Baseline for Detecting Misclassified and Out-of-Distribution Examples
  in Neural Networks
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Dan Hendrycks
Kevin Gimpel
UQCV
179
3,482
0
07 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
218
6,172
0
01 Jul 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.3K
194,641
0
10 Dec 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCVBDL
883
9,364
0
06 Jun 2015
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