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

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

13 January 2021

Niloufar Salehi

Aditya G. Parameswaran

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

17 / 17 papers shown

Title
EDCA - An Evolutionary Data-Centric AutoML Framework for Efficient Pipelines Joana Simões João Correia 156 0 0 06 Mar 2025
Data Makes Better Data Scientists Jinjin Zhao A. Gal Sanjay Krishnan 26 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 50 1 0 23 May 2024
Towards a Non-Ideal Methodological Framework for Responsible ML Ramaravind Kommiya Mothilal Shion Guha Syed Ishtiaque Ahmed 42 7 0 20 Jan 2024
Assessing the Use of AutoML for Data-Driven Software Engineering Fabio Calefato L. Quaranta F. Lanubile Marcos Kalinowski 32 7 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 55 23 0 17 Apr 2023
Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data Work Jennifer Rogers Anamaria Crisan 16 7 0 05 Apr 2023
Addressing UX Practitioners' Challenges in Designing ML Applications: an Interactive Machine Learning Approach K. J. Kevin Feng David W. McDonald HAI 17 11 0 23 Feb 2023
AutoML in The Wild: Obstacles, Workarounds, and Expectations Yuan Sun Qiurong Song Xinning Gui Fenglong Ma Ting Wang 21 13 0 21 Feb 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 31 21 0 02 Feb 2023
The Grind for Good Data: Understanding ML Practitioners' Struggles and Aspirations in Making Good Data Inha Cha Juhyun Oh Cheul Young Park Jiyoon Han Hwalsuk Lee 29 2 0 28 Nov 2022
Operationalizing Machine Learning: An Interview Study Shreya Shankar Rolando Garcia J. M. Hellerstein Aditya G. Parameswaran 68 51 0 16 Sep 2022
An Empirical Study on the Usage of Automated Machine Learning Tools Forough Majidi Moses Openja Foutse Khomh Heng Li 42 14 0 28 Aug 2022
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work Chengbo Zheng Dakuo Wang A. Wang Xiaojuan Ma 17 52 0 21 Mar 2022
Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines Nikolay O. Nikitin Pavel Vychuzhanin M. Sarafanov Iana S. Polonskaia I. Revin Irina V. Barabanova G. Maximov Anna V. Kaluzhnaya A. Boukhanovsky 18 55 0 26 Jun 2021
Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development Angela Lee Doris Xin D. Lee Aditya G. Parameswaran HAI 49 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 178 308 0 05 Sep 2019