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Interpretable Two-level Boolean Rule Learning for Classification

Interpretable Two-level Boolean Rule Learning for Classification

18 June 2016
Guolong Su
Dennis L. Wei
Kush R. Varshney
Dmitry Malioutov
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Papers citing "Interpretable Two-level Boolean Rule Learning for Classification"

13 / 13 papers shown
Title
Analogies and Feature Attributions for Model Agnostic Explanation of
  Similarity Learners
Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners
Karthikeyan N. Ramamurthy
Amit Dhurandhar
Dennis L. Wei
Zaid Bin Tariq
FAtt
45
3
0
02 Feb 2022
Model Learning with Personalized Interpretability Estimation (ML-PIE)
Model Learning with Personalized Interpretability Estimation (ML-PIE)
M. Virgolin
A. D. Lorenzo
Francesca Randone
Eric Medvet
M. Wahde
24
30
0
13 Apr 2021
AIST: An Interpretable Attention-based Deep Learning Model for Crime
  Prediction
AIST: An Interpretable Attention-based Deep Learning Model for Crime Prediction
Yeasir Rayhan
T. Hashem
24
22
0
16 Dec 2020
Discovering Drug-Drug and Drug-Disease Interactions Inducing Acute
  Kidney Injury Using Deep Rule Forests
Discovering Drug-Drug and Drug-Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests
Bowen Kuo
Yihuang Kang
Pinghsung Wu
Sheng-Tai Huang
Yajie Huang
7
2
0
04 Jul 2020
Model Agnostic Multilevel Explanations
Model Agnostic Multilevel Explanations
Karthikeyan N. Ramamurthy
B. Vinzamuri
Yunfeng Zhang
Amit Dhurandhar
29
41
0
12 Mar 2020
Learning Global Transparent Models Consistent with Local Contrastive
  Explanations
Learning Global Transparent Models Consistent with Local Contrastive Explanations
Tejaswini Pedapati
Avinash Balakrishnan
Karthikeyan Shanmugam
Amit Dhurandhar
FAtt
22
0
0
19 Feb 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
23
34
0
07 Jan 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies,
  Opportunities and Challenges toward Responsible AI
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta
Natalia Díaz Rodríguez
Javier Del Ser
Adrien Bennetot
Siham Tabik
...
S. Gil-Lopez
Daniel Molina
Richard Benjamins
Raja Chatila
Francisco Herrera
XAI
41
6,125
0
22 Oct 2019
Model Agnostic Contrastive Explanations for Structured Data
Model Agnostic Contrastive Explanations for Structured Data
Amit Dhurandhar
Tejaswini Pedapati
Avinash Balakrishnan
Pin-Yu Chen
Karthikeyan Shanmugam
Ruchi Puri
FAtt
25
82
0
31 May 2019
Leveraging Latent Features for Local Explanations
Leveraging Latent Features for Local Explanations
Ronny Luss
Pin-Yu Chen
Amit Dhurandhar
P. Sattigeri
Yunfeng Zhang
Karthikeyan Shanmugam
Chun-Chen Tu
FAtt
54
37
0
29 May 2019
The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to
  Optimal Pattern Recognition with Propositional Logic
The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic
Ole-Christoffer Granmo
17
148
0
04 Apr 2018
A Formal Framework to Characterize Interpretability of Procedures
A Formal Framework to Characterize Interpretability of Procedures
Amit Dhurandhar
Vijay Iyengar
Ronny Luss
Karthikeyan Shanmugam
15
19
0
12 Jul 2017
Efficient Data Representation by Selecting Prototypes with Importance
  Weights
Efficient Data Representation by Selecting Prototypes with Importance Weights
Karthik S. Gurumoorthy
Amit Dhurandhar
Guillermo Cecchi
Charu Aggarwal
29
22
0
05 Jul 2017
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