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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"

44 / 244 papers shown
Title
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge
Kai Xu
Dae Hoon Park
Chang Yi
Charles Sutton
HAIFAtt
69
26
0
11 Mar 2018
Learning Rules-First Classifiers
Learning Rules-First Classifiers
Deborah Cohen
Amit Daniely
Amir Globerson
G. Elidan
38
0
0
08 Mar 2018
Global Model Interpretation via Recursive Partitioning
Global Model Interpretation via Recursive Partitioning
Chengliang Yang
Anand Rangarajan
Sanjay Ranka
FAtt
65
80
0
11 Feb 2018
A Survey Of Methods For Explaining Black Box Models
A Survey Of Methods For Explaining Black Box Models
Riccardo Guidotti
A. Monreale
Salvatore Ruggieri
Franco Turini
D. Pedreschi
F. Giannotti
XAI
243
4,010
0
06 Feb 2018
How do Humans Understand Explanations from Machine Learning Systems? An
  Evaluation of the Human-Interpretability of Explanation
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation
Menaka Narayanan
Emily Chen
Jeffrey He
Been Kim
S. Gershman
Finale Doshi-Velez
FAttXAI
110
243
0
02 Feb 2018
Considerations When Learning Additive Explanations for Black-Box Models
Considerations When Learning Additive Explanations for Black-Box Models
S. Tan
Giles Hooker
Paul Koch
Albert Gordo
R. Caruana
FAtt
113
24
0
26 Jan 2018
What do we need to build explainable AI systems for the medical domain?
What do we need to build explainable AI systems for the medical domain?
Andreas Holzinger
Chris Biemann
C. Pattichis
D. Kell
91
694
0
28 Dec 2017
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to
  Stop Worrying and Love the Social and Behavioural Sciences
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences
Tim Miller
Piers Howe
L. Sonenberg
AI4TSSyDa
88
374
0
02 Dec 2017
QCBA: Improving Rule Classifiers Learned from Quantitative Data by
  Recovering Information Lost by Discretisation
QCBA: Improving Rule Classifiers Learned from Quantitative Data by Recovering Information Lost by Discretisation
Tomáš Kliegr
E. Izquierdo
25
4
0
28 Nov 2017
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment
  Effect
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect
Tong Wang
Cynthia Rudin
CMLBDL
97
27
0
16 Oct 2017
Multi-Value Rule Sets
Multi-Value Rule Sets
Tong Wang
40
6
0
15 Oct 2017
An Optimization Approach to Learning Falling Rule Lists
An Optimization Approach to Learning Falling Rule Lists
Chaofan Chen
Cynthia Rudin
86
39
0
06 Oct 2017
Embedding Deep Networks into Visual Explanations
Embedding Deep Networks into Visual Explanations
Zhongang Qi
Saeed Khorram
Fuxin Li
41
27
0
15 Sep 2017
Interpretable Categorization of Heterogeneous Time Series Data
Interpretable Categorization of Heterogeneous Time Series Data
Ritchie Lee
Mykel J. Kochenderfer
Ole J. Mengshoel
Joshua Silbermann
71
28
0
30 Aug 2017
Using Program Induction to Interpret Transition System Dynamics
Using Program Induction to Interpret Transition System Dynamics
Svetlin Penkov
S. Ramamoorthy
AI4CE
66
11
0
26 Jul 2017
Proxy Non-Discrimination in Data-Driven Systems
Proxy Non-Discrimination in Data-Driven Systems
Anupam Datta
Matt Fredrikson
Gihyuk Ko
Piotr (Peter) Mardziel
S. Sen
33
47
0
25 Jul 2017
Interpretable & Explorable Approximations of Black Box Models
Interpretable & Explorable Approximations of Black Box Models
Himabindu Lakkaraju
Ece Kamar
R. Caruana
J. Leskovec
FAtt
104
254
0
04 Jul 2017
Interpretability via Model Extraction
Interpretability via Model Extraction
Osbert Bastani
Carolyn Kim
Hamsa Bastani
FAtt
78
129
0
29 Jun 2017
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
296
2,278
0
24 Jun 2017
MAGIX: Model Agnostic Globally Interpretable Explanations
MAGIX: Model Agnostic Globally Interpretable Explanations
Nikaash Puri
Piyush B. Gupta
Pratiksha Agarwal
Sukriti Verma
Balaji Krishnamurthy
FAtt
111
41
0
22 Jun 2017
Interpreting Blackbox Models via Model Extraction
Interpreting Blackbox Models via Model Extraction
Osbert Bastani
Carolyn Kim
Hamsa Bastani
FAtt
135
173
0
23 May 2017
Explaining Transition Systems through Program Induction
Explaining Transition Systems through Program Induction
Svetlin Penkov
S. Ramamoorthy
66
5
0
23 May 2017
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine
  Learnt Programs
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs
Anupam Datta
Matt Fredrikson
Gihyuk Ko
Piotr (Peter) Mardziel
S. Sen
67
63
0
22 May 2017
PreCog: Improving Crowdsourced Data Quality Before Acquisition
PreCog: Improving Crowdsourced Data Quality Before Acquisition
H. Nilforoshan
Jiannan Wang
Eugene Wu
24
3
0
07 Apr 2017
Learning Certifiably Optimal Rule Lists for Categorical Data
Learning Certifiably Optimal Rule Lists for Categorical Data
E. Angelino
Nicholas Larus-Stone
Daniel Alabi
Margo Seltzer
Cynthia Rudin
137
195
0
06 Apr 2017
Rationalization: A Neural Machine Translation Approach to Generating
  Natural Language Explanations
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations
Upol Ehsan
Brent Harrison
Larry Chan
Mark O. Riedl
139
221
0
25 Feb 2017
Simple rules for complex decisions
Simple rules for complex decisions
Jongbin Jung
Connor Concannon
Ravi Shroff
Sharad Goel
D. Goldstein
CML
85
105
0
15 Feb 2017
Towards Better Analysis of Machine Learning Models: A Visual Analytics
  Perspective
Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective
Shixia Liu
Xiting Wang
Mengchen Liu
Jun Zhu
HAI
65
366
0
04 Feb 2017
Nonlinear network-based quantitative trait prediction from
  transcriptomic data
Nonlinear network-based quantitative trait prediction from transcriptomic data
Emilie Devijver
M. Gallopin
Émeline Perthame
74
8
0
26 Jan 2017
Computing Human-Understandable Strategies
Computing Human-Understandable Strategies
Sam Ganzfried
Farzana Yusuf
18
9
0
19 Dec 2016
Learning Cost-Effective and Interpretable Regimes for Treatment
  Recommendation
Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation
Himabindu Lakkaraju
Cynthia Rudin
OffRL
21
6
0
23 Nov 2016
Programs as Black-Box Explanations
Programs as Black-Box Explanations
Sameer Singh
Marco Tulio Ribeiro
Carlos Guestrin
FAtt
73
55
0
22 Nov 2016
Towards the Science of Security and Privacy in Machine Learning
Towards the Science of Security and Privacy in Machine Learning
Nicolas Papernot
Patrick McDaniel
Arunesh Sinha
Michael P. Wellman
AAML
99
474
0
11 Nov 2016
Variational Bayes In Private Settings (VIPS)
Variational Bayes In Private Settings (VIPS)
Mijung Park
James R. Foulds
Kamalika Chaudhuri
Max Welling
105
42
0
01 Nov 2016
Learning Cost-Effective Treatment Regimes using Markov Decision
  Processes
Learning Cost-Effective Treatment Regimes using Markov Decision Processes
Himabindu Lakkaraju
Cynthia Rudin
38
9
0
21 Oct 2016
Learning Optimized Risk Scores
Learning Optimized Risk Scores
Berk Ustun
Cynthia Rudin
208
84
0
01 Oct 2016
Model-Agnostic Interpretability of Machine Learning
Model-Agnostic Interpretability of Machine Learning
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
90
840
0
16 Jun 2016
Rationalizing Neural Predictions
Rationalizing Neural Predictions
Tao Lei
Regina Barzilay
Tommi Jaakkola
131
813
0
13 Jun 2016
Scalable Bayesian Rule Lists
Scalable Bayesian Rule Lists
Hongyu Yang
Cynthia Rudin
Margo Seltzer
TPM
76
212
0
27 Feb 2016
"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
1.3K
17,197
0
16 Feb 2016
Sparse Density Trees and Lists: An Interpretable Alternative to
  High-Dimensional Histograms
Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms
Siong Thye Goh
Lesia Semenova
Cynthia Rudin
TPM
48
1
0
22 Oct 2015
Directional Decision Lists
Directional Decision Lists
M. Goessling
Shan Kang
40
2
0
30 Aug 2015
Or's of And's for Interpretable Classification, with Application to
  Context-Aware Recommender Systems
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems
Tong Wang
Cynthia Rudin
Finale Doshi-Velez
Yimin Liu
Erica Klampfl
P. MacNeille
55
41
0
28 Apr 2015
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning
  and Prototype Classification
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
Been Kim
Cynthia Rudin
J. Shah
101
321
0
03 Mar 2015
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