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Regional Tree Regularization for Interpretability in Black Box Models
v1v2v3 (latest)

Regional Tree Regularization for Interpretability in Black Box Models

AAAI Conference on Artificial Intelligence (AAAI), 2019
13 August 2019
Mike Wu
S. Parbhoo
M. C. Hughes
R. Kindle
Leo Anthony Celi
Maurizio Zazzi
Volker Roth
Finale Doshi-Velez
ArXiv (abs)PDFHTML

Papers citing "Regional Tree Regularization for Interpretability in Black Box Models"

18 / 18 papers shown
VISIONLOGIC: From Neuron Activations to Causally Grounded Concept Rules for Vision Models
VISIONLOGIC: From Neuron Activations to Causally Grounded Concept Rules for Vision Models
Chuqin Geng
Yuhe Jiang
Ziyu Zhao
Haolin Ye
Zhaoyue Wang
X. Si
Xujie Si
NAIFAttVLM
333
1
0
13 Mar 2025
Tree-Based Leakage Inspection and Control in Concept Bottleneck Models
Tree-Based Leakage Inspection and Control in Concept Bottleneck Models
Angelos Ragkousis
Sonali Parbhoo
243
7
0
08 Oct 2024
Enabling Regional Explainability by Automatic and Model-agnostic Rule
  Extraction
Enabling Regional Explainability by Automatic and Model-agnostic Rule Extraction
Yu Chen
Tianyu Cui
Alexander Capstick
Nan Fletcher-Loyd
Payam Barnaghi
279
1
0
25 Jun 2024
A Design Trajectory Map of Human-AI Collaborative Reinforcement Learning
  Systems: Survey and Taxonomy
A Design Trajectory Map of Human-AI Collaborative Reinforcement Learning Systems: Survey and Taxonomy
Zhaoxing Li
223
2
0
16 May 2024
CA-Stream: Attention-based pooling for interpretable image recognition
CA-Stream: Attention-based pooling for interpretable image recognition
Felipe Torres
Hanwei Zhang
R. Sicre
Stéphane Ayache
Yannis Avrithis
263
3
0
23 Apr 2024
An Interpretable Power System Transient Stability Assessment Method with
  Expert Guiding Neural-Regression-Tree
An Interpretable Power System Transient Stability Assessment Method with Expert Guiding Neural-Regression-Tree
Hanxuan Wang
Na Lu
Zixuan Wang
Jiacheng Liu
Jun Liu
129
0
0
03 Apr 2024
Interpretable Reinforcement Learning for Robotics and Continuous Control
Interpretable Reinforcement Learning for Robotics and Continuous Control
Rohan R. Paleja
Letian Chen
Yaru Niu
Andrew Silva
Zhaoxin Li
...
K. Chang
H. E. Tseng
Yan Wang
S. Nageshrao
Matthew C. Gombolay
247
9
0
16 Nov 2023
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning
Going Beyond XAI: A Systematic Survey for Explanation-Guided LearningACM Computing Surveys (ACM CSUR), 2022
Yuyang Gao
Siyi Gu
Junji Jiang
S. Hong
Dazhou Yu
Bo Pan
326
65
0
07 Dec 2022
Computing Abductive Explanations for Boosted Trees
Computing Abductive Explanations for Boosted TreesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Gilles Audemard
Jean-Marie Lagniez
Pierre Marquis
N. Szczepanski
226
23
0
16 Sep 2022
A Survey of Neural Trees
A Survey of Neural Trees
Haoling Li
Mingli Song
Mengqi Xue
Haofei Zhang
Jingwen Ye
Lechao Cheng
Weilong Dai
AI4CE
374
6
0
07 Sep 2022
Leveraging Explanations in Interactive Machine Learning: An Overview
Leveraging Explanations in Interactive Machine Learning: An OverviewFrontiers in Artificial Intelligence (FAI), 2022
Stefano Teso
Öznur Alkan
Wolfgang Stammer
Elizabeth M. Daly
XAIFAttLRM
592
79
0
29 Jul 2022
NN2Rules: Extracting Rule List from Neural Networks
NN2Rules: Extracting Rule List from Neural Networks
G. R. Lal
Varun Mithal
114
2
0
04 Jul 2022
The Health Gym: Synthetic Health-Related Datasets for the Development of
  Reinforcement Learning Algorithms
The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning AlgorithmsScientific Data (Sci Data), 2022
N. Kuo
Mark Polizzotto
S. Finfer
Federico Garcia
Anders Sönnerborg
Maurizio Zazzi
Michael Böhm
Louisa R Jorm
S. Barbieri
OOD
184
32
0
12 Mar 2022
Learning Interpretable, High-Performing Policies for Autonomous Driving
Learning Interpretable, High-Performing Policies for Autonomous Driving
Rohan R. Paleja
Yaru Niu
Andrew Silva
Chace Ritchie
Sugju Choi
Matthew C. Gombolay
375
18
0
04 Feb 2022
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic
  Review on Evaluating Explainable AI
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIACM Computing Surveys (ACM CSUR), 2022
Meike Nauta
Jan Trienes
Shreyasi Pathak
Elisa Nguyen
Michelle Peters
Yasmin Schmitt
Jorg Schlotterer
M. V. Keulen
C. Seifert
ELMXAI
735
613
0
20 Jan 2022
Defense Against Explanation Manipulation
Defense Against Explanation Manipulation
Ruixiang Tang
Ninghao Liu
Fan Yang
Na Zou
Helen Zhou
AAML
227
13
0
08 Nov 2021
A Survey on Neural Network Interpretability
A Survey on Neural Network InterpretabilityIEEE Transactions on Emerging Topics in Computational Intelligence (IEEE TETCI), 2020
Yu Zhang
Peter Tiño
A. Leonardis
Shengcai Liu
FaMLXAI
626
860
0
28 Dec 2020
On Explaining Decision Trees
On Explaining Decision Trees
Yacine Izza
Alexey Ignatiev
Sasha Rubin
FAtt
236
107
0
21 Oct 2020
1
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