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Defuse: Harnessing Unrestricted Adversarial Examples for Debugging
  Models Beyond Test Accuracy

Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy

11 February 2021
Dylan Slack
N. Rauschmayr
K. Kenthapadi
    AAML
ArXivPDFHTML

Papers citing "Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy"

3 / 3 papers shown
Title
Amazon SageMaker Clarify: Machine Learning Bias Detection and
  Explainability in the Cloud
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
Michaela Hardt
Xiaoguang Chen
Xiaoyi Cheng
Michele Donini
J. Gelman
...
Muhammad Bilal Zafar
Sanjiv Ranjan Das
Kevin Haas
Tyler Hill
K. Kenthapadi
ELM
FaML
36
42
0
07 Sep 2021
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data
  and Bayesian Inference
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
Disi Ji
Padhraic Smyth
M. Steyvers
34
45
0
19 Oct 2020
Constructing Unrestricted Adversarial Examples with Generative Models
Constructing Unrestricted Adversarial Examples with Generative Models
Yang Song
Rui Shu
Nate Kushman
Stefano Ermon
GAN
AAML
185
302
0
21 May 2018
1