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Logic-Based Explainability: Past, Present & Future

Logic-Based Explainability: Past, Present & Future

4 June 2024
Joao Marques-Silva
ArXiv (abs)PDFHTML

Papers citing "Logic-Based Explainability: Past, Present & Future"

31 / 31 papers shown
Title
Interpretable DNFs
Interpretable DNFs
Martin C. Cooper
Imane Bousdira
Clément Carbonnel
FAttAI4CE
40
0
0
27 May 2025
Locally-Minimal Probabilistic Explanations
Locally-Minimal Probabilistic Explanations
Yacine Izza
Kuldeep S. Meel
Sasha Rubin
47
3
0
19 Dec 2023
The Pros and Cons of Adversarial Robustness
The Pros and Cons of Adversarial Robustness
Yacine Izza
Sasha Rubin
AAML
43
1
0
18 Dec 2023
Axiomatic Aggregations of Abductive Explanations
Axiomatic Aggregations of Abductive Explanations
Gagan Biradar
Yacine Izza
Elita Lobo
Vignesh Viswanathan
Yair Zick
FAtt
71
6
0
29 Sep 2023
Explainability is NOT a Game
Explainability is NOT a Game
Sasha Rubin
Xuanxiang Huang
62
19
0
27 Jun 2023
First Three Years of the International Verification of Neural Networks
  Competition (VNN-COMP)
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)
Christopher Brix
Mark Niklas Muller
Stanley Bak
Taylor T. Johnson
Changliu Liu
NAI
66
74
0
14 Jan 2023
On Computing Probabilistic Abductive Explanations
On Computing Probabilistic Abductive Explanations
Yacine Izza
Xuanxiang Huang
Alexey Ignatiev
Nina Narodytska
Martin C. Cooper
Sasha Rubin
FAttXAI
93
20
0
12 Dec 2022
Feature Necessity & Relevancy in ML Classifier Explanations
Feature Necessity & Relevancy in ML Classifier Explanations
Xuanxiang Huang
Martin C. Cooper
António Morgado
Jordi Planes
Sasha Rubin
FAtt
67
19
0
27 Oct 2022
Logic-Based Explainability in Machine Learning
Logic-Based Explainability in Machine Learning
Sasha Rubin
LRMXAI
117
40
0
24 Oct 2022
Computing Abductive Explanations for Boosted Trees
Computing Abductive Explanations for Boosted Trees
Gilles Audemard
Jean-Marie Lagniez
Pierre Marquis
N. Szczepanski
74
14
0
16 Sep 2022
On Computing Relevant Features for Explaining NBCs
On Computing Relevant Features for Explaining NBCs
Yacine Izza
Sasha Rubin
84
5
0
11 Jul 2022
ASTERYX : A model-Agnostic SaT-basEd appRoach for sYmbolic and
  score-based eXplanations
ASTERYX : A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanations
Ryma Boumazouza
Fahima Cheikh
Bertrand Mazure
Karim Tabia
47
32
0
23 Jun 2022
SAT-Based Rigorous Explanations for Decision Lists
SAT-Based Rigorous Explanations for Decision Lists
Alexey Ignatiev
Sasha Rubin
XAI
61
46
0
14 May 2021
On Guaranteed Optimal Robust Explanations for NLP Models
On Guaranteed Optimal Robust Explanations for NLP Models
Emanuele La Malfa
A. Zbrzezny
Rhiannon Michelmore
Nicola Paoletti
Marta Z. Kwiatkowska
FAtt
59
48
0
08 May 2021
Towards Rigorous Interpretations: a Formalisation of Feature Attribution
Towards Rigorous Interpretations: a Formalisation of Feature Attribution
Darius Afchar
Romain Hennequin
Vincent Guigue
FAtt
83
20
0
26 Apr 2021
On the Computational Intelligibility of Boolean Classifiers
On the Computational Intelligibility of Boolean Classifiers
Gilles Audemard
S. Bellart
Louenas Bounia
F. Koriche
Jean-Marie Lagniez
Pierre Marquis
45
58
0
13 Apr 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
226
673
0
20 Mar 2021
On Explaining Decision Trees
On Explaining Decision Trees
Yacine Izza
Alexey Ignatiev
Sasha Rubin
FAtt
79
88
0
21 Oct 2020
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time
  and Delay
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
Sasha Rubin
Thomas Gerspacher
Martin C. Cooper
Alexey Ignatiev
Nina Narodytska
FAtt
58
63
0
13 Aug 2020
Three Modern Roles for Logic in AI
Three Modern Roles for Logic in AI
Adnan Darwiche
AI4CE
115
56
0
18 Apr 2020
Problems with Shapley-value-based explanations as feature importance
  measures
Problems with Shapley-value-based explanations as feature importance measures
Indra Elizabeth Kumar
Suresh Venkatasubramanian
C. Scheidegger
Sorelle A. Friedler
TDIFAtt
88
366
0
25 Feb 2020
On The Reasons Behind Decisions
On The Reasons Behind Decisions
Adnan Darwiche
Auguste Hirth
FaML
54
149
0
21 Feb 2020
A Symbolic Approach to Explaining Bayesian Network Classifiers
A Symbolic Approach to Explaining Bayesian Network Classifiers
Andy Shih
Arthur Choi
Adnan Darwiche
FAtt
74
243
0
09 May 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
143
3,970
0
06 Feb 2018
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
293
2,267
0
24 Jun 2017
Explanation in Artificial Intelligence: Insights from the Social
  Sciences
Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller
XAI
252
4,273
0
22 Jun 2017
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
1.1K
22,018
0
22 May 2017
The Mythos of Model Interpretability
The Mythos of Model Interpretability
Zachary Chase Lipton
FaML
183
3,706
0
10 Jun 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
1.2K
17,033
0
16 Feb 2016
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAMLGAN
282
19,121
0
20 Dec 2014
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
282
14,963
1
21 Dec 2013
1