Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows

13 January 2021

Papers citing "Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows"

8 / 8 papers shown

Title
EDCA - An Evolutionary Data-Centric AutoML Framework for Efficient Pipelines Joana Simões João Correia 58 0 0 06 Mar 2025
Data Makes Better Data Scientists Jinjin Zhao A. Gal Sanjay Krishnan 20 2 0 27 May 2024
Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Science Practitioners' Perceptions in Human&AI-Assisted Feature Engineering Design Qian Zhu Dakuo Wang Shuai Ma April Yi Wang Zixin Chen Udayan Khurana Xiaojuan Ma 31 1 0 23 May 2024
Assessing the Use of AutoML for Data-Driven Software Engineering Fabio Calefato L. Quaranta F. Lanubile Marcos Kalinowski 16 5 0 20 Jul 2023
Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling Haotian Li Yun Wang Q. V. Liao Huamin Qu 42 23 0 17 Apr 2023
Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service" Kornel Lewicki M. S. Lee Jennifer Cobbe Jatinder Singh 11 20 0 02 Feb 2023
Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development Angela Lee Doris Xin D. Lee Aditya G. Parameswaran HAI 32 12 0 04 May 2020
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 153 312 0 05 Sep 2019