ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2306.14786
  4. Cited By
Evolutionary approaches to explainable machine learning

Evolutionary approaches to explainable machine learning

23 June 2023
Ryan Zhou
Ting-Kuei Hu
ArXiv (abs)PDFHTML

Papers citing "Evolutionary approaches to explainable machine learning"

14 / 14 papers shown
Title
On genetic programming representations and fitness functions for
  interpretable dimensionality reduction
On genetic programming representations and fitness functions for interpretable dimensionality reduction
Thomas Uriot
M. Virgolin
Tanja Alderliesten
Peter A. N. Bosman
25
9
0
01 Mar 2022
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and
  Future Opportunities
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities
Waddah Saeed
C. Omlin
XAI
88
442
0
11 Nov 2021
Applying Genetic Programming to Improve Interpretability in Machine
  Learning Models
Applying Genetic Programming to Improve Interpretability in Machine Learning Models
Leonardo Augusto Ferreira
F. G. Guimarães
Rodrigo C. P. Silva
33
37
0
18 May 2020
Multi-Objective Counterfactual Explanations
Multi-Objective Counterfactual Explanations
Susanne Dandl
Christoph Molnar
Martin Binder
B. Bischl
68
260
0
23 Apr 2020
Genetic Programming for Evolving a Front of Interpretable Models for
  Data Visualisation
Genetic Programming for Evolving a Front of Interpretable Models for Data Visualisation
Andrew Lensen
Bing Xue
Mengjie Zhang
36
43
0
27 Jan 2020
On Explaining Machine Learning Models by Evolving Crucial and Compact
  Features
On Explaining Machine Learning Models by Evolving Crucial and Compact Features
M. Virgolin
Tanja Alderliesten
Peter A. N. Bosman
60
28
0
04 Jul 2019
Local Rule-Based Explanations of Black Box Decision Systems
Local Rule-Based Explanations of Black Box Decision Systems
Riccardo Guidotti
A. Monreale
Salvatore Ruggieri
D. Pedreschi
Franco Turini
F. Giannotti
141
439
0
28 May 2018
Generating Natural Language Adversarial Examples
Generating Natural Language Adversarial Examples
M. Alzantot
Yash Sharma
Ahmed Elgohary
Bo-Jhang Ho
Mani B. Srivastava
Kai-Wei Chang
AAML
417
933
0
21 Apr 2018
Counterfactual Explanations without Opening the Black Box: Automated
  Decisions and the GDPR
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
Sandra Wachter
Brent Mittelstadt
Chris Russell
MLAU
138
2,371
0
01 Nov 2017
Interpretable & Explorable Approximations of Black Box Models
Interpretable & Explorable Approximations of Black Box Models
Himabindu Lakkaraju
Ece Kamar
R. Caruana
J. Leskovec
FAtt
79
254
0
04 Jul 2017
The Mythos of Model Interpretability
The Mythos of Model Interpretability
Zachary Chase Lipton
FaML
183
3,716
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
17,071
0
16 Feb 2016
Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images
Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images
Aravindh Mahendran
Andrea Vedaldi
FAtt
95
536
0
07 Dec 2015
Determinantal point processes for machine learning
Determinantal point processes for machine learning
Alex Kulesza
B. Taskar
272
1,140
0
25 Jul 2012
1