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On Explaining Decision Trees

On Explaining Decision Trees

21 October 2020
Yacine Izza
Alexey Ignatiev
Sasha Rubin
    FAtt
ArXivPDFHTML

Papers citing "On Explaining Decision Trees"

15 / 15 papers shown
Title
A Mathematical Philosophy of Explanations in Mechanistic Interpretability -- The Strange Science Part I.i
A Mathematical Philosophy of Explanations in Mechanistic Interpretability -- The Strange Science Part I.i
Kola Ayonrinde
Louis Jaburi
MILM
86
1
0
01 May 2025
Logic for Explainable AI
Logic for Explainable AI
Adnan Darwiche
30
8
0
09 May 2023
Logic-Based Explainability in Machine Learning
Logic-Based Explainability in Machine Learning
Sasha Rubin
LRM
XAI
47
39
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
26
12
0
16 Sep 2022
On Computing Relevant Features for Explaining NBCs
On Computing Relevant Features for Explaining NBCs
Yacine Izza
Sasha Rubin
28
5
0
11 Jul 2022
From global to local MDI variable importances for random forests and
  when they are Shapley values
From global to local MDI variable importances for random forests and when they are Shapley values
Antonio Sutera
Gilles Louppe
V. A. Huynh-Thu
L. Wehenkel
Pierre Geurts
FAtt
23
7
0
03 Nov 2021
Foundations of Symbolic Languages for Model Interpretability
Foundations of Symbolic Languages for Model Interpretability
Marcelo Arenas
Daniel Baez
Pablo Barceló
Jorge A. Pérez
Bernardo Subercaseaux
ReLM
LRM
21
24
0
05 Oct 2021
On Efficiently Explaining Graph-Based Classifiers
On Efficiently Explaining Graph-Based Classifiers
Xuanxiang Huang
Yacine Izza
Alexey Ignatiev
Sasha Rubin
FAtt
28
37
0
02 Jun 2021
Probabilistic Sufficient Explanations
Probabilistic Sufficient Explanations
Eric Wang
Pasha Khosravi
Guy Van den Broeck
XAI
FAtt
TPM
22
22
0
21 May 2021
SAT-Based Rigorous Explanations for Decision Lists
SAT-Based Rigorous Explanations for Decision Lists
Alexey Ignatiev
Sasha Rubin
XAI
9
44
0
14 May 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
17
56
0
13 Apr 2021
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
29
0
13 Apr 2021
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
14
58
0
13 Aug 2020
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
234
2,238
0
24 Jun 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
51
195
0
06 Apr 2017
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