Workshop Overview

The Automated Machine Learning Market Research Report from 2021 predicts a tremendous growth in the automated machine learning market over the next decade. “The major factors driving the market are the burgeoning requirement for efficient fraud detection solutions, soaring demand for personalized product recommendations, and increasing need for predictive lead scoring.” The report also suggests that the COVID-19 pandemic has accelerated the increase in digital business models: “with many healthcare companies adopting machine-learning-enabled chatbots to enable the contactless screening of COVID-19 symptoms.” AutoML continues to generate very current and widespread attention regarding appropriate uses, current capabilities, limitations, challenges, and future potential (Forbes, 2021-02-23; Waring, J. et. al., 2020). SIGKDD's mission is to provide the premier forum for advancement, education, and adoption of the "science" of knowledge discovery and data mining from all types of data stored in computers and networks of computers. AutoML is the method of automating the process of knowledge discovery and data mining. With the dramatic growth in both dataset sizes and data mining algorithms' complexity, and with the critical importance of efficient and effective application of machine learning to today’s most difficult problems, AutoML is not only relevant but crucial to the mission of SIGKDD.

The goals of the AutoML workshop are:

● To identify opportunities and challenges for automation in machine learning

● To provide an opportunity for researchers to discuss best practices for automation in machine learning, potentially leading to definition of standards

● To provide a forum for researchers to speak out and debate on different ideas in the area of automation in machine learning


Technical Sponsors:

● RTP ACM Chapter
https://sites.google.com/view/rtpacmchapter/home


Call For Content

We request extended abstracts (2-4 pages) or full-length papers (up to 10 pages) be submitted by May 26, 2022. Accepted abstracts will be presented as oral and/or poster presentations.

Topics include (but are not limited to):

● Hyperparameter autotuning of machine learning algorithms
● Neural Architecture Search (NAS)
● Internet of things (IoT) and automation
● Automation bias and misuse
● Automated assessment of fairness in model predictive accuracy
● Automated methods:
o in machine learning, data mining, predictive analytics, and deep learning
o in healthcare and medical diagnosis
o in autonomous vehicles
o in machine learning pipelines and process flows of production systems
o in big data applications
o for interpretable machine learning
o for fake news detection
o for adversarial robustness
o for monitoring and updating models
o for streaming data
o for large-scale modeling
o for data preparation and feature engineering
o for variable selection and model selection


Submission Instructions

Either an extended abstract (2-4 pages) or a full-length paper (up to 10 pages) is required to be considered for this workshop (submission of both is not required). Use of the ACM Proceedings Format (https://www.acm.org/publications/proceedings-template) is recommended.

All submissions will be peer-reviewed. If accepted, at least one author should attend the workshop to present their work. The papers should be in PDF format and submitted via EasyChair: https://easychair.org/conferences/?conf=automl2022


Important Dates

May 26, 2022: Due date for paper/abstract submissions
June 20, 2022: Notification of acceptance to authors
TBD: Final submission due
August 14-17, 2022: Workshop (exact day of workshop not yet determined)

Contact Us

For any questions, please email the organizing committee at ai.ml.automation@gmail.com