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Efficient Exploration of the Rashomon Set of Rule Set Models

Efficient Exploration of the Rashomon Set of Rule Set Models

5 June 2024
Martino Ciaperoni
Han Xiao
Aristides Gionis
ArXiv (abs)PDFHTML

Papers citing "Efficient Exploration of the Rashomon Set of Rule Set Models"

11 / 11 papers shown
Title
Exploring the Whole Rashomon Set of Sparse Decision Trees
Exploring the Whole Rashomon Set of Sparse Decision Trees
Rui Xin
Chudi Zhong
Zhi Chen
Takuya Takagi
Margo Seltzer
Cynthia Rudin
65
58
0
16 Sep 2022
Regularized impurity reduction: Accurate decision trees with complexity
  guarantees
Regularized impurity reduction: Accurate decision trees with complexity guarantees
Guangyi Zhang
Aristides Gionis
28
13
0
23 Aug 2022
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
218
673
0
20 Mar 2021
Characterizing Fairness Over the Set of Good Models Under Selective
  Labels
Characterizing Fairness Over the Set of Good Models Under Selective Labels
Amanda Coston
Ashesh Rambachan
Alexandra Chouldechova
FaML
93
85
0
02 Jan 2021
A Survey on the Explainability of Supervised Machine Learning
A Survey on the Explainability of Supervised Machine Learning
Nadia Burkart
Marco F. Huber
FaMLXAI
55
775
0
16 Nov 2020
Diverse Rule Sets
Diverse Rule Sets
Guangyi Zhang
Aristides Gionis
43
19
0
17 Jun 2020
"How do I fool you?": Manipulating User Trust via Misleading Black Box
  Explanations
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations
Himabindu Lakkaraju
Osbert Bastani
65
255
0
15 Nov 2019
Predictive Multiplicity in Classification
Predictive Multiplicity in Classification
Charles Marx
Flavio du Pin Calmon
Berk Ustun
123
146
0
14 Sep 2019
On the Existence of Simpler Machine Learning Models
On the Existence of Simpler Machine Learning Models
Lesia Semenova
Cynthia Rudin
Ronald E. Parr
61
86
0
05 Aug 2019
MLIC: A MaxSAT-Based framework for learning interpretable classification
  rules
MLIC: A MaxSAT-Based framework for learning interpretable classification rules
Dmitry Malioutov
Kuldeep S. Meel
50
44
0
05 Dec 2018
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
97
195
0
06 Apr 2017
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