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PermuteAttack: Counterfactual Explanation of Machine Learning Credit
  Scorecards

PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards

24 August 2020
Masoud Hashemi
Ali Fathi
    AAML
ArXivPDFHTML

Papers citing "PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards"

7 / 7 papers shown
Title
Modeling Tabular data using Conditional GAN
Modeling Tabular data using Conditional GAN
Lei Xu
Maria Skoularidou
Alfredo Cuesta-Infante
K. Veeramachaneni
CML
MU
SyDa
GAN
75
1,232
0
01 Jul 2019
Detecting Overfitting via Adversarial Examples
Detecting Overfitting via Adversarial Examples
Roman Werpachowski
András Gyorgy
Csaba Szepesvári
TDI
43
45
0
06 Mar 2019
GAIN: Missing Data Imputation using Generative Adversarial Nets
GAIN: Missing Data Imputation using Generative Adversarial Nets
Jinsung Yoon
James Jordon
M. Schaar
GAN
37
1,001
0
07 Jun 2018
Explanations based on the Missing: Towards Contrastive Explanations with
  Pertinent Negatives
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Amit Dhurandhar
Pin-Yu Chen
Ronny Luss
Chun-Chen Tu
Pai-Shun Ting
Karthikeyan Shanmugam
Payel Das
FAtt
89
587
0
21 Feb 2018
Counterfactual Explanations without Opening the Black Box: Automated
  Decisions and the GDPR
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
Sandra Wachter
Brent Mittelstadt
Chris Russell
MLAU
57
2,332
0
01 Nov 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection
  Methods
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini
D. Wagner
AAML
101
1,851
0
20 May 2017
The Mythos of Model Interpretability
The Mythos of Model Interpretability
Zachary Chase Lipton
FaML
106
3,672
0
10 Jun 2016
1