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1606.05798
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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"
29 / 29 papers shown
Explainable Evidential Clustering
Victor F. Lopes de Souza
Karima Bakhti
Sofiane Ramdani
Denis Mottet
Abdelhak Imoussaten
CML
278
1
0
16 Jul 2025
XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics
Shahid Alam
Zeynep Altıparmak
383
10
0
04 Feb 2024
Boolformer: Symbolic Regression of Logic Functions with Transformers
Stéphane dÁscoli
Arthur Renard
Josh Susskind
Samy Bengio
J. Susskind
Emmanuel Abbe
303
7
0
21 Sep 2023
Explainable AI using expressive Boolean formulas
Machine Learning and Knowledge Extraction (MLKE), 2023
G. Rosenberg
J. K. Brubaker
M. Schuetz
Grant Salton
Zhihuai Zhu
E. Zhu
Serdar Kadioğlu
S. E. Borujeni
H. Katzgraber
252
12
0
06 Jun 2023
Neural-based classification rule learning for sequential data
International Conference on Learning Representations (ICLR), 2023
Marine Collery
Philippe Bonnard
Franccois Fages
R. Kusters
123
4
0
22 Feb 2023
Trade-off Between Efficiency and Consistency for Removal-based Explanations
Neural Information Processing Systems (NeurIPS), 2022
Yifan Zhang
Haowei He
Zhiyuan Tan
Yang Yuan
FAtt
543
7
0
31 Oct 2022
Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners
Karthikeyan N. Ramamurthy
Amit Dhurandhar
Dennis L. Wei
Zaid Bin Tariq
FAtt
256
3
0
02 Feb 2022
On the Effectiveness of Interpretable Feedforward Neural Network
IEEE International Joint Conference on Neural Network (IJCNN), 2021
Miles Q. Li
Benjamin C. M. Fung
Adel Abusitta
FaML
AI4CE
134
5
0
03 Nov 2021
Model Learning with Personalized Interpretability Estimation (ML-PIE)
M. Virgolin
A. D. Lorenzo
Francesca Randone
Eric Medvet
M. Wahde
433
34
0
13 Apr 2021
Learning Accurate and Interpretable Decision Rule Sets from Neural Networks
AAAI Conference on Artificial Intelligence (AAAI), 2021
Litao Qiao
Weijia Wang
Bill Lin
FaML
165
52
0
04 Mar 2021
AIST: An Interpretable Attention-based Deep Learning Model for Crime Prediction
Yeasir Rayhan
T. Hashem
265
37
0
16 Dec 2020
A Survey on the Explainability of Supervised Machine Learning
Journal of Artificial Intelligence Research (JAIR), 2020
Nadia Burkart
Marco F. Huber
FaML
XAI
416
928
0
16 Nov 2020
Principles and Practice of Explainable Machine Learning
Frontiers in Big Data (Front. Big Data), 2020
Vaishak Belle
I. Papantonis
FaML
295
552
0
18 Sep 2020
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
95
2
0
04 Jul 2020
Diverse Rule Sets
Guangyi Zhang
Aristides Gionis
143
24
0
17 Jun 2020
Explainable Artificial Intelligence: a Systematic Review
Giulia Vilone
Luca Longo
XAI
755
308
0
29 May 2020
Model Agnostic Multilevel Explanations
Neural Information Processing Systems (NeurIPS), 2020
Karthikeyan N. Ramamurthy
B. Vinzamuri
Yunfeng Zhang
Amit Dhurandhar
248
48
0
12 Mar 2020
Learning Global Transparent Models Consistent with Local Contrastive Explanations
Tejaswini Pedapati
Avinash Balakrishnan
Karthikeyan Shanmugam
Amit Dhurandhar
FAtt
314
0
0
19 Feb 2020
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules
AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2019
Bishwamittra Ghosh
Kuldeep S. Meel
244
36
0
07 Jan 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Information Fusion (Inf. Fusion), 2019
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
1.1K
8,083
0
22 Oct 2019
Model Agnostic Contrastive Explanations for Structured Data
Amit Dhurandhar
Tejaswini Pedapati
Avinash Balakrishnan
Pin-Yu Chen
Karthikeyan Shanmugam
Ruchi Puri
FAtt
339
92
0
31 May 2019
Leveraging Latent Features for Local Explanations
Knowledge Discovery and Data Mining (KDD), 2019
Ronny Luss
Pin-Yu Chen
Amit Dhurandhar
P. Sattigeri
Yunfeng Zhang
Karthikeyan Shanmugam
Chun-Chen Tu
FAtt
352
37
0
29 May 2019
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
Cynthia Rudin
ELM
FaML
337
231
0
26 Nov 2018
The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic
Ole-Christoffer Granmo
654
188
0
04 Apr 2018
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Amit Dhurandhar
Pin-Yu Chen
Ronny Luss
Chun-Chen Tu
Pai-Shun Ting
Karthikeyan Shanmugam
Payel Das
FAtt
501
663
0
21 Feb 2018
A Survey Of Methods For Explaining Black Box Models
Riccardo Guidotti
A. Monreale
Salvatore Ruggieri
Franco Turini
D. Pedreschi
F. Giannotti
XAI
855
4,703
0
06 Feb 2018
A Formal Framework to Characterize Interpretability of Procedures
Amit Dhurandhar
Vijay Iyengar
Ronny Luss
Karthikeyan Shanmugam
135
19
0
12 Jul 2017
Efficient Data Representation by Selecting Prototypes with Importance Weights
Karthik S. Gurumoorthy
Amit Dhurandhar
Guillermo Cecchi
Charu Aggarwal
410
22
0
05 Jul 2017
TIP: Typifying the Interpretability of Procedures
Amit Dhurandhar
Vijay Iyengar
Ronny Luss
Karthikeyan Shanmugam
337
35
0
09 Jun 2017
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