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1511.01644
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Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
5 November 2015
Benjamin Letham
Cynthia Rudin
Tyler H. McCormick
D. Madigan
FAtt
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Papers citing
"Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model"
50 / 244 papers shown
Title
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
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Robust subgroup discovery
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Peter Grünwald
Thomas Bäck
M. Leeuwen
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Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Cynthia Rudin
Chaofan Chen
Zhi Chen
Haiyang Huang
Lesia Semenova
Chudi Zhong
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245
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0
20 Mar 2021
CACTUS: Detecting and Resolving Conflicts in Objective Functions
Subhajit Das
Alex Endert
53
0
0
13 Mar 2021
Learning Accurate and Interpretable Decision Rule Sets from Neural Networks
Litao Qiao
Weijia Wang
Bill Lin
FaML
34
40
0
04 Mar 2021
Visualizing Rule Sets: Exploration and Validation of a Design Space
Jun Yuan
O. Nov
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44
1
0
01 Mar 2021
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence
Atoosa Kasirzadeh
70
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01 Mar 2021
Benchmarking and Survey of Explanation Methods for Black Box Models
F. Bodria
F. Giannotti
Riccardo Guidotti
Francesca Naretto
D. Pedreschi
S. Rinzivillo
XAI
123
234
0
25 Feb 2021
3D4ALL: Toward an Inclusive Pipeline to Classify 3D Contents
Nahyun Kwon
Chen Liang
Jeeeun Kim
DiffM
36
1
0
24 Feb 2021
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
Sushant Agarwal
S. Jabbari
Chirag Agarwal
Sohini Upadhyay
Zhiwei Steven Wu
Himabindu Lakkaraju
FAtt
AAML
82
64
0
21 Feb 2021
VitrAI -- Applying Explainable AI in the Real World
Marc Hanussek
Falko Kötter
Maximilien Kintz
Jens Drawehn
37
2
0
12 Feb 2021
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond
Guang Yang
Qinghao Ye
Jun Xia
141
504
0
03 Feb 2021
Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations
Danding Wang
Wencan Zhang
Brian Y. Lim
FAtt
56
22
0
23 Jan 2021
Dissonance Between Human and Machine Understanding
Zijian Zhang
Jaspreet Singh
U. Gadiraju
Avishek Anand
122
74
0
18 Jan 2021
Absolute Value Constraint: The Reason for Invalid Performance Evaluation Results of Neural Network Models for Stock Price Prediction
Yi Wei
102
1
0
10 Jan 2021
Learning Interpretable Concept-Based Models with Human Feedback
Isaac Lage
Finale Doshi-Velez
49
25
0
04 Dec 2020
A Survey on the Explainability of Supervised Machine Learning
Nadia Burkart
Marco F. Huber
FaML
XAI
77
782
0
16 Nov 2020
Robust and Stable Black Box Explanations
Himabindu Lakkaraju
Nino Arsov
Osbert Bastani
AAML
FAtt
65
84
0
12 Nov 2020
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
S. McGrath
Parth Mehta
Alexandra Zytek
Isaac Lage
Himabindu Lakkaraju
UD
68
26
0
12 Nov 2020
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions
Kasun Amarasinghe
Kit Rodolfa
Hemank Lamba
Rayid Ghani
ELM
XAI
187
53
0
27 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
Christoph Molnar
Giuseppe Casalicchio
B. Bischl
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114
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0
19 Oct 2020
A general approach to compute the relevance of middle-level input features
Andrea Apicella
Salvatore Giugliano
Francesco Isgrò
R. Prevete
49
6
0
16 Oct 2020
Learning Binary Decision Trees by Argmin Differentiation
Valentina Zantedeschi
Matt J. Kusner
Vlad Niculae
62
13
0
09 Oct 2020
Simplifying the explanation of deep neural networks with sufficient and necessary feature-sets: case of text classification
Florentin Flambeau Jiechieu Kameni
Norbert Tsopzé
XAI
FAtt
MedIm
26
1
0
08 Oct 2020
Explainability via Responsibility
Faraz Khadivpour
Matthew J. Guzdial
23
2
0
04 Oct 2020
A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing
Ping Lang
Xiongjun Fu
M. Martorella
Jian Dong
Rui Qin
Xianpeng Meng
M. Xie
41
42
0
29 Sep 2020
Explainable Predictive Process Monitoring
Musabir Musabayli
F. Maggi
Williams Rizzi
Josep Carmona
Chiara Di Francescomarino
68
61
0
04 Aug 2020
A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution
A. Khademi
Vasant Honavar
CML
39
9
0
01 Aug 2020
Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification
Cristina Garbacea
Mengtian Guo
Samuel Carton
Qiaozhu Mei
60
28
0
31 Jul 2020
Event Prediction in the Big Data Era: A Systematic Survey
Liang Zhao
AI4TS
102
56
0
19 Jul 2020
Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context
Ehsan Toreini
Mhairi Aitken
Kovila P. L. Coopamootoo
Karen Elliott
Vladimiro González-Zelaya
P. Missier
Magdalene Ng
Aad van Moorsel
74
18
0
17 Jul 2020
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing
Max Biggs
Wei-Ju Sun
M. Ettl
75
35
0
03 Jul 2020
Causality Learning: A New Perspective for Interpretable Machine Learning
Guandong Xu
Tri Dung Duong
Q. Li
S. Liu
Xianzhi Wang
XAI
OOD
CML
62
52
0
27 Jun 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey
Arun Das
P. Rad
XAI
179
607
0
16 Jun 2020
A Formal Language Approach to Explaining RNNs
Bishwamittra Ghosh
Daniel Neider
40
1
0
12 Jun 2020
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts
R. Akrour
Davide Tateo
Jan Peters
60
22
0
10 Jun 2020
Explainable Artificial Intelligence: a Systematic Review
Giulia Vilone
Luca Longo
XAI
110
271
0
29 May 2020
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks
Arash Rahnama
A.-Yu Tseng
FAtt
AAML
FaML
46
5
0
20 May 2020
Post-hoc explanation of black-box classifiers using confident itemsets
M. Moradi
Matthias Samwald
139
101
0
05 May 2020
Interpretable Random Forests via Rule Extraction
Clément Bénard
Gérard Biau
Sébastien Da Veiga
Erwan Scornet
47
59
0
29 Apr 2020
Valid Explanations for Learning to Rank Models
Jaspreet Singh
Zhenye Wang
Megha Khosla
Avishek Anand
LRM
FAtt
43
8
0
29 Apr 2020
Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs
Sungsoo Ray Hong
Jessica Hullman
E. Bertini
HAI
92
197
0
23 Apr 2020
A New Method to Compare the Interpretability of Rule-based Algorithms
Vincent Margot
G. Luta
FAtt
20
17
0
03 Apr 2020
A Hierarchy of Limitations in Machine Learning
M. Malik
68
57
0
12 Feb 2020
Leveraging Rationales to Improve Human Task Performance
Devleena Das
Sonia Chernova
77
50
0
11 Feb 2020
An interpretable neural network model through piecewise linear approximation
Mengzhuo Guo
Qingpeng Zhang
Xiuwu Liao
D. Zeng
MILM
FAtt
51
8
0
20 Jan 2020
Interpretation and Simplification of Deep Forest
Sangwon Kim
Mira Jeong
ByoungChul Ko
FAtt
98
8
0
14 Jan 2020
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules
Bishwamittra Ghosh
Kuldeep S. Meel
75
35
0
07 Jan 2020
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization
Zhuo Wang
Wei Zhang
Ning Liu
Jianyong Wang
44
30
0
10 Dec 2019
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
Vanessa Buhrmester
David Münch
Michael Arens
MLAU
FaML
XAI
AAML
117
368
0
27 Nov 2019
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