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AutoML in The Wild: Obstacles, Workarounds, and Expectations

AutoML in The Wild: Obstacles, Workarounds, and Expectations

21 February 2023
Yuan Sun
Qiurong Song
Xinning Gui
Fenglong Ma
Ting Wang
ArXivPDFHTML

Papers citing "AutoML in The Wild: Obstacles, Workarounds, and Expectations"

11 / 11 papers shown
Title
Position: A Call to Action for a Human-Centered AutoML Paradigm
Position: A Call to Action for a Human-Centered AutoML Paradigm
Marius Lindauer
Florian Karl
A. Klier
Julia Moosbauer
Alexander Tornede
Andreas Mueller
Frank Hutter
Matthias Feurer
Bernd Bischl
41
6
0
05 Jun 2024
Generative AI in the Wild: Prospects, Challenges, and Strategies
Generative AI in the Wild: Prospects, Challenges, and Strategies
Yuan Sun
Eunchae Jang
Fenglong Ma
Ting Wang
34
21
0
03 Apr 2024
Evolving machine learning workflows through interactive AutoML
Evolving machine learning workflows through interactive AutoML
Rafael Barbudo
Aurora Ramírez
José Raúl Romero
16
1
0
28 Feb 2024
Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual
  Framework, Potentials and Limitations
Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations
Mario Truss
Marc Schmitt
21
1
0
06 Feb 2024
Assessing the Use of AutoML for Data-Driven Software Engineering
Assessing the Use of AutoML for Data-Driven Software Engineering
Fabio Calefato
L. Quaranta
F. Lanubile
Marcos Kalinowski
32
7
0
20 Jul 2023
Trustworthy, responsible, ethical AI in manufacturing and supply chains:
  synthesis and emerging research questions
Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions
Alexandra Brintrup
George Baryannis
Ashutosh Tiwari
S. Ratchev
Giovanna Martínez-Arellano
Jatinder Singh
26
3
0
19 May 2023
Whither AutoML? Understanding the Role of Automation in Machine Learning
  Workflows
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows
Doris Xin
Eva Yiwei Wu
D. Lee
Niloufar Salehi
Aditya G. Parameswaran
50
91
0
13 Jan 2021
AutoML to Date and Beyond: Challenges and Opportunities
AutoML to Date and Beyond: Challenges and Opportunities
Shubhra (Santu) Karmaker
Md. Mahadi Hassan
Micah J. Smith
Lei Xu
Chengxiang Zhai
K. Veeramachaneni
73
225
0
21 Oct 2020
Trust in Data Science: Collaboration, Translation, and Accountability in
  Corporate Data Science Projects
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects
Samir Passi
S. Jackson
171
108
0
09 Feb 2020
Human-AI Collaboration in Data Science: Exploring Data Scientists'
  Perceptions of Automated AI
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
Dakuo Wang
Justin D. Weisz
Michael J. Muller
Parikshit Ram
Werner Geyer
Casey Dugan
Y. Tausczik
Horst Samulowitz
Alexander G. Gray
178
308
0
05 Sep 2019
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
323
4,212
0
23 Aug 2019
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