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A Concept and Argumentation based Interpretable Model in High Risk Domains

A Concept and Argumentation based Interpretable Model in High Risk Domains

Haixiao Chi
Dawei Wang
Gaojie Cui
Feng Mao
Beishui Liao
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Papers citing "A Concept and Argumentation based Interpretable Model in High Risk Domains"

11 / 11 papers shown
Title
Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics
Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics
Amirata Ghorbani
Dina Berenbaum
Maor Ivgi
Yuval Dafna
James Zou
FAtt
45
9
0
10 Nov 2021
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
C. Chang
R. Caruana
Anna Goldenberg
AI4CE
93
80
0
03 Jun 2021
How Interpretable and Trustworthy are GAMs?
How Interpretable and Trustworthy are GAMs?
C. Chang
S. Tan
Benjamin J. Lengerich
Anna Goldenberg
R. Caruana
FAtt
121
79
0
11 Jun 2020
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
Nils Reimers
Iryna Gurevych
104
1,032
0
21 Apr 2020
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
Dylan Slack
Sophie Hilgard
Emily Jia
Sameer Singh
Himabindu Lakkaraju
FAttAAMLMLAU
81
822
0
06 Nov 2019
An Interpretable Model with Globally Consistent Explanations for Credit Risk
An Interpretable Model with Globally Consistent Explanations for Credit Risk
Chaofan Chen
Kangcheng Lin
Cynthia Rudin
Yaron Shaposhnik
Sijia Wang
Tong Wang
FAtt
81
93
0
30 Nov 2018
This Looks Like That: Deep Learning for Interpretable Image Recognition
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen
Oscar Li
Chaofan Tao
A. Barnett
Jonathan Su
Cynthia Rudin
260
1,187
0
27 Jun 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim
Martin Wattenberg
Justin Gilmer
Carrie J. Cai
James Wexler
F. Viégas
Rory Sayres
FAtt
248
1,849
0
30 Nov 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
1.2K
17,071
0
16 Feb 2016
Empirical Evaluation of Rectified Activations in Convolutional Network
Empirical Evaluation of Rectified Activations in Convolutional Network
Bing Xu
Naiyan Wang
Tianqi Chen
Mu Li
142
2,916
0
05 May 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
469
43,357
0
11 Feb 2015