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Interpretable classifiers using rules and Bayesian analysis: Building a
  better stroke prediction model

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
ArXiv (abs)PDFHTML

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
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
David S. Watson
Limor Gultchin
Ankur Taly
Luciano Floridi
81
64
0
27 Mar 2021
Robust subgroup discovery
Robust subgroup discovery
Hugo Manuel Proença
Peter Grünwald
Thomas Bäck
M. Leeuwen
54
13
0
25 Mar 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand
  Challenges
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Cynthia Rudin
Chaofan Chen
Zhi Chen
Haiyang Huang
Lesia Semenova
Chudi Zhong
FaMLAI4CELRM
245
677
0
20 Mar 2021
CACTUS: Detecting and Resolving Conflicts in Objective Functions
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
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
Visualizing Rule Sets: Exploration and Validation of a Design Space
Jun Yuan
O. Nov
E. Bertini
44
1
0
01 Mar 2021
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable
  Artificial Intelligence
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence
Atoosa Kasirzadeh
70
24
0
01 Mar 2021
Benchmarking and Survey of Explanation Methods for Black Box Models
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
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
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
Sushant Agarwal
S. Jabbari
Chirag Agarwal
Sohini Upadhyay
Zhiwei Steven Wu
Himabindu Lakkaraju
FAttAAML
82
64
0
21 Feb 2021
VitrAI -- Applying Explainable AI in the Real World
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
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
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
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
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
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
A Survey on the Explainability of Supervised Machine Learning
Nadia Burkart
Marco F. Huber
FaMLXAI
77
782
0
16 Nov 2020
Robust and Stable Black Box Explanations
Robust and Stable Black Box Explanations
Himabindu Lakkaraju
Nino Arsov
Osbert Bastani
AAMLFAtt
65
84
0
12 Nov 2020
When Does Uncertainty Matter?: Understanding the Impact of Predictive
  Uncertainty in ML Assisted Decision Making
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
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions
Kasun Amarasinghe
Kit Rodolfa
Hemank Lamba
Rayid Ghani
ELMXAI
187
53
0
27 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and
  Challenges
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
Christoph Molnar
Giuseppe Casalicchio
B. Bischl
AI4TSAI4CE
114
405
0
19 Oct 2020
A general approach to compute the relevance of middle-level input
  features
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
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
Simplifying the explanation of deep neural networks with sufficient and necessary feature-sets: case of text classification
Florentin Flambeau Jiechieu Kameni
Norbert Tsopzé
XAIFAttMedIm
26
1
0
08 Oct 2020
Explainability via Responsibility
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
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
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 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
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
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
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
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
Causality Learning: A New Perspective for Interpretable Machine Learning
Guandong Xu
Tri Dung Duong
Q. Li
S. Liu
Xianzhi Wang
XAIOODCML
62
52
0
27 Jun 2020
Opportunities and Challenges in Explainable Artificial Intelligence
  (XAI): A Survey
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
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
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
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
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks
Arash Rahnama
A.-Yu Tseng
FAttAAMLFaML
46
5
0
20 May 2020
Post-hoc explanation of black-box classifiers using confident itemsets
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
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
Valid Explanations for Learning to Rank Models
Jaspreet Singh
Zhenye Wang
Megha Khosla
Avishek Anand
LRMFAtt
43
8
0
29 Apr 2020
Human Factors in Model Interpretability: Industry Practices, Challenges,
  and Needs
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
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
A Hierarchy of Limitations in Machine Learning
M. Malik
68
57
0
12 Feb 2020
Leveraging Rationales to Improve Human Task Performance
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
An interpretable neural network model through piecewise linear approximation
Mengzhuo Guo
Qingpeng Zhang
Xiuwu Liao
D. Zeng
MILMFAtt
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
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
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
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
Vanessa Buhrmester
David Münch
Michael Arens
MLAUFaMLXAIAAML
117
368
0
27 Nov 2019
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