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A Hierarchy of Limitations in Machine Learning
v1v2 (latest)

A Hierarchy of Limitations in Machine Learning

12 February 2020
M. Malik
ArXiv (abs)PDFHTML

Papers citing "A Hierarchy of Limitations in Machine Learning"

14 / 14 papers shown
Every Character Counts: From Vulnerability to Defense in Phishing Detection
Every Character Counts: From Vulnerability to Defense in Phishing Detection
Maria Chiper
Radu Tudor Ionescu
247
0
0
24 Sep 2025
Combining Self-attention and Dilation Convolutional for Semantic Segmentation of Coal Maceral Groups
Combining Self-attention and Dilation Convolutional for Semantic Segmentation of Coal Maceral GroupsIEEE Internet of Things Journal (IEEE IoT J.), 2025
Zhenghao Xi
Zhengnan Lv
Yang Zheng
Xiang Liu
Zhuang Yu
Junran Chen
Jing Hu
Yaqi Liu
DiffM
177
0
0
15 Jun 2025
Specification Overfitting in Artificial Intelligence
Specification Overfitting in Artificial IntelligenceArtificial Intelligence Review (Artif Intell Rev), 2024
Benjamin Roth
Pedro Henrique Luz de Araujo
Yuxi Xia
Saskia Kaltenbrunner
Christoph Korab
660
15
0
13 Mar 2024
Tackling Cyberattacks through AI-based Reactive Systems: A Holistic
  Review and Future Vision
Tackling Cyberattacks through AI-based Reactive Systems: A Holistic Review and Future Vision
Sergio Bernardez Molina
Pantaleone Nespoli
Félix Gómez Mármol
AAML
164
8
0
11 Dec 2023
REFORMS: Reporting Standards for Machine Learning Based Science
REFORMS: Reporting Standards for Machine Learning Based Science
Sayash Kapoor
Emily F. Cantrell
Kenny Peng
Thanh Hien Pham
C. Bail
...
Matthew J. Salganik
Marta Serra-Garcia
Brandon M Stewart
Gilles Vandewiele
Arvind Narayanan
366
30
0
15 Aug 2023
Concrete Safety for ML Problems: System Safety for ML Development and
  Assessment
Concrete Safety for ML Problems: System Safety for ML Development and Assessment
Edgar W. Jatho
L. Mailloux
Eugene D. Williams
P. McClure
Joshua A. Kroll
173
1
0
06 Feb 2023
System Safety Engineering for Social and Ethical ML Risks: A Case Study
System Safety Engineering for Social and Ethical ML Risks: A Case Study
Edgar W. Jatho
L. Mailloux
Shalaleh Rismani
Eugene D. Williams
Joshua A. Kroll
276
2
0
08 Nov 2022
Leakage and the Reproducibility Crisis in ML-based Science
Leakage and the Reproducibility Crisis in ML-based Science
Sayash Kapoor
Arvind Narayanan
204
221
0
14 Jul 2022
Rethinking Fairness: An Interdisciplinary Survey of Critiques of
  Hegemonic ML Fairness Approaches
Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness ApproachesJournal of Artificial Intelligence Research (JAIR), 2022
Lindsay Weinberg
FaMLSyDa
156
84
0
06 May 2022
The worst of both worlds: A comparative analysis of errors in learning
  from data in psychology and machine learning
The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learningAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2022
Jessica Hullman
Sayash Kapoor
Priyanka Nanayakkara
Andrew Gelman
Arvind Narayanan
649
44
0
12 Mar 2022
AI and the Everything in the Whole Wide World Benchmark
AI and the Everything in the Whole Wide World Benchmark
Inioluwa Deborah Raji
Emily M. Bender
Amandalynne Paullada
Emily L. Denton
A. Hanna
289
426
0
26 Nov 2021
How to avoid machine learning pitfalls: a guide for academic researchers
How to avoid machine learning pitfalls: a guide for academic researchersPatterns (Patterns), 2021
M. Lones
VLMFaMLOnRL
560
135
0
05 Aug 2021
Limitations of machine learning for building energy prediction: ASHRAE
  Great Energy Predictor III Kaggle competition error analysis
Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis
Clayton Miller
Bianca Picchetti
Chunlei Fu
Jovan Pantelic
AI4CE
292
32
0
25 Jun 2021
Mapping the Internet: Modelling Entity Interactions in Complex
  Heterogeneous Networks
Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks
Šimon Mandlík
Tomás Pevný
270
6
0
19 Apr 2021
1
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