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2009.11698
Cited By
Principles and Practice of Explainable Machine Learning
Frontiers in Big Data (Front. Big Data), 2020
18 September 2020
Vaishak Belle
I. Papantonis
FaML
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Papers citing
"Principles and Practice of Explainable Machine Learning"
40 / 90 papers shown
Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework
Peter E. D. Love
J. Matthews
Weili Fang
Stuart Porter
Hanbin Luo
L. Ding
155
4
0
12 Nov 2022
TEFL: Turbo Explainable Federated Learning for 6G Trustworthy Zero-Touch Network Slicing
Swastika Roy
Hatim Chergui
C. Verikoukis
160
3
0
18 Oct 2022
A Survey on Explainable Anomaly Detection
ACM Transactions on Knowledge Discovery from Data (TKDD), 2022
Zhong Li
Yuxuan Zhu
M. Leeuwen
352
134
0
13 Oct 2022
On Explainability in AI-Solutions: A Cross-Domain Survey
S. D. Antón
Daniel Schneider
Hans D. Schotten
207
6
0
11 Oct 2022
AI, Opacity, and Personal Autonomy
Philosophy & Technology (PT), 2022
Bram Vaassen
FaML
MLAU
131
51
0
25 Sep 2022
Active learning-assisted neutron spectroscopy with log-Gaussian processes
Nature Communications (Nat Commun), 2022
Mario Teixeira Parente
G. Brandl
C. Franz
U. Stuhr
M. Ganeva
A. Schneidewind
166
12
0
02 Sep 2022
A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks
Expert systems with applications (ESWA), 2022
Abhilash Singh
J. Amutha
Jaiprakash Nagar
Sandeep Sharma
139
37
0
25 Aug 2022
Unravelling Interlanguage Facts via Explainable Machine Learning
Digital Scholarship in the Humanities (DSH), 2022
B. Berti
Andrea Esuli
Fabrizio Sebastiani
56
3
0
02 Aug 2022
Leveraging Explanations in Interactive Machine Learning: An Overview
Frontiers in Artificial Intelligence (FAI), 2022
Stefano Teso
Öznur Alkan
Wolfgang Stammer
Elizabeth M. Daly
XAI
FAtt
LRM
528
78
0
29 Jul 2022
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
BMC Bioinformatics (BB), 2022
Magdalena Wysocka
Oskar Wysocki
Marie Zufferey
Dónal Landers
André Freitas
AI4CE
361
49
0
02 Jul 2022
A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting
Frontiers in Water (FW), 2022
Georgia Papacharalampous
Hristos Tyralis
AI4CE
274
35
0
17 Jun 2022
Pest presence prediction using interpretable machine learning
Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2022
Ornela Nanushi
Vasileios Sitokonstantinou
Ilias Tsoumas
C. Kontoes
70
9
0
16 May 2022
EVOTER: Evolution of Transparent Explainable Rule-sets
ACM Transactions on Evolutionary Learning and Optimization (TELO), 2022
Hormoz Shahrzad
Babak Hodjat
Risto Miikkulainen
319
6
0
21 Apr 2022
An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival
Sören Schleibaum
J. Muller
Monika Sester
AI4TS
211
4
0
17 Mar 2022
Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box
ACM Computing Surveys (ACM CSUR), 2022
Catarina Moreira
Yu-Liang Chou
Chih-Jou Hsieh
Chun Ouyang
Joaquim A. Jorge
João Pereira
CML
398
12
0
04 Mar 2022
Explainability in Machine Learning: a Pedagogical Perspective
Andreas Bueff
I. Papantonis
Auste Simkute
Vaishak Belle
169
3
0
21 Feb 2022
XAI in the context of Predictive Process Monitoring: Too much to Reveal
Ghada Elkhawaga
Mervat Abuelkheir
M. Reichert
130
1
0
16 Feb 2022
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
Applied Sciences (Appl. Sci.), 2022
Ghada Elkhawaga
Mervat Abuelkheir
M. Reichert
235
16
0
16 Feb 2022
AnoMili: Spoofing Prevention and Explainable Anomaly Detection for the 1553 Military Avionic Bus
Efrat Levy
Nadav Maman
A. Shabtai
Yuval Elovici
102
15
0
14 Feb 2022
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities
Xin Du
Bénédicte Legastelois
B. Ganesh
A. Rajan
Hana Chockler
Vaishak Belle
Stuart Anderson
S. Ramamoorthy
AAML
205
6
0
27 Jan 2022
Principled Diverse Counterfactuals in Multilinear Models
Machine-mediated learning (ML), 2022
I. Papantonis
Vaishak Belle
AAML
149
3
0
17 Jan 2022
Hydroclimatic time series features at multiple time scales
Georgia Papacharalampous
Hristos Tyralis
Y. Markonis
M. Hanel
AI4TS
167
3
0
02 Dec 2021
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities
Knowledge-Based Systems (KBS), 2021
Waddah Saeed
C. Omlin
XAI
278
589
0
11 Nov 2021
Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities
O. Kuiper
M. V. D. Berg
Joost van den Burgt
S. Leijnen
129
37
0
03 Nov 2021
TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models
S. Chatterjee
Arnab Das
Chirag Mandal
Budhaditya Mukhopadhyay
Manish Vipinraj
Aniruddh Shukla
R. Rao
Chompunuch Sarasaen
Oliver Speck
A. Nürnberger
MedIm
224
16
0
16 Oct 2021
Robotic Lever Manipulation using Hindsight Experience Replay and Shapley Additive Explanations
Sindre Benjamin Remman
A. Lekkas
123
15
0
07 Oct 2021
Attention-like feature explanation for tabular data
International Journal of Data Science and Analysis (JDSA), 2021
A. Konstantinov
Lev V. Utkin
FAtt
309
5
0
10 Aug 2021
Desiderata for Explainable AI in statistical production systems of the European Central Bank
Carlos Navarro
Georgios Kanellos
Thomas Gottron
191
10
0
18 Jul 2021
Trustworthy AI: A Computational Perspective
Haochen Liu
Yiqi Wang
Wenqi Fan
Xiaorui Liu
Yaxin Li
Shaili Jain
Yunhao Liu
Anil K. Jain
Shucheng Zhou
FaML
419
260
0
12 Jul 2021
Explainable AI: current status and future directions
Prashant Gohel
Priyanka Singh
M. Mohanty
XAI
257
114
0
12 Jul 2021
An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data
Lev V. Utkin
A. Konstantinov
Kirill Vishniakov
FAtt
356
7
0
16 Jun 2021
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods
PeerJ Computer Science (PeerJ Comput. Sci.), 2021
E. Amparore
Alan Perotti
P. Bajardi
FAtt
191
79
0
01 Jun 2021
Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Models
B. La Rosa
Roberto Capobianco
Daniele Nardi
VLM
159
10
0
01 Jun 2021
SurvNAM: The machine learning survival model explanation
Neural Networks (NN), 2021
Lev V. Utkin
Egor D. Satyukov
A. Konstantinov
AAML
FAtt
215
36
0
18 Apr 2021
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
Information Fusion (Inf. Fusion), 2021
Yu-Liang Chou
Catarina Moreira
P. Bruza
Chun Ouyang
Joaquim A. Jorge
CML
384
220
0
07 Mar 2021
Ensembles of Random SHAPs
Lev V. Utkin
A. Konstantinov
FAtt
198
22
0
04 Mar 2021
Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees
IEEE Robotics and Automation Letters (RA-L), 2021
Pradyumna Tambwekar
Andrew Silva
N. Gopalan
Matthew C. Gombolay
268
10
0
18 Jan 2021
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions
Data & Policy (DP), 2020
Kasun Amarasinghe
Kit Rodolfa
Hemank Lamba
Rayid Ghani
ELM
XAI
506
69
0
27 Oct 2020
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines
A. Konstantinov
Lev V. Utkin
FedML
AI4CE
177
194
0
14 Oct 2020
Explainable Empirical Risk Minimization
Linli Zhang
Georgios Karakasidis
Arina Odnoblyudova
Leyla Dogruel
Alex Jung
182
5
0
03 Sep 2020
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