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An Interpretable Probabilistic Approach for Demystifying Black-box
  Predictive Models

An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models

21 July 2020
Catarina Moreira
Yu-Liang Chou
M. Velmurugan
Chun Ouyang
Renuka Sindhgatta
P. Bruza
ArXivPDFHTML

Papers citing "An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models"

3 / 3 papers shown
Title
Generative Perturbation Analysis for Probabilistic Black-Box Anomaly
  Attribution
Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution
T. Idé
Naoki Abe
33
4
0
09 Aug 2023
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
225
3,681
0
28 Feb 2017
Learning Representations for Counterfactual Inference
Learning Representations for Counterfactual Inference
Fredrik D. Johansson
Uri Shalit
David Sontag
CML
OOD
BDL
212
719
0
12 May 2016
1