The Third International Workshop on Automation in Machine Learning
AutoML 2019
- URL: https://sites.google.com/view/automl2019-workshop/home
- Event Date: 2019-08-05 ~ 2019-08-05
- Submission Date: 2019-05-05
Workshop Overview
According to Forbes in December of 2018, one of the 5 Artificial Intelligence Trends To Watch Out For In 2019 is the gain in prominence of automated machine learning. The term AutoML is appearing more and more in data science discussions, publications, applications, and systems, as an aid to build better machine learning models. AutoML is being used in autonomous driving applications, sales forecasting and lead prioritization systems, and in many generic systems to generate and optimize machine learning pipelines that can select features, transform data, select the best model type and optimize hyperparameters. The debates continue regarding the level to which data science can and should be automated, the level of machine learning knowledge and expertise needed to build quality models, and the where and when manual intervention is necessary, yet the development and application of approaches and tools to automate repeated tasks continues to increase. The advancement, education, and adoption of data mining and machine learning practices require a transformation of theory to application, and feedback from application to theory. The development of tools to automate data mining efforts fosters this transformation and feedback and also promotes the development of standards and the adoption of these standards. Automated standards enable researchers and practitioners to better communicate, sharing successes and challenges in a more consistent common language. In an age of software as a service and ever-increasing scalability requirements, standards are necessary. Consistent adoption, application, and communication in turn promote research and refinement of the automated strategies and growth of the community. To keep pace with the rapidly increasing volume and rate of data generation, standardization and automating of data mining activities are critical. The challenges that must be discussed relate to the boundaries of automated tasks and individual attention needed for each unique business and data scenario.
This workshop will be held in Conjunction with KDD 2019.
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
• IEEE SMC Human Perception in Multimedia Computing
http://www.ieeesmc.org/technical-activities/human-machine-systems/human-perception-in-multimedia-computing
Call For Content
We request either full papers (up to 10 pages, 6 to 8 pages are recommended) or extended abstracts (2-4 pages) be submitted by May 5, 2019. Accepted papers and abstracts will be presented as oral presentations.
A Best Paper Award will be presented during the workshop.
Topics include (but are not limited to):
• Automation and optimization
• Hyperparameter autotuning of machine learning algorithms
• Internet of things (IoT) and automation
• Automation bias
• Automation misuse
• Automated methods:
· in machine learning, data mining, predictive analytics, and deep learning
· in knowledge discovery in databases
· in autonomous vehicles
· in machine learning pipelines and process flows of production systems
· in big data applications
· for monitoring and updating models
· to detect fake news
· for streaming data
· for interpretable machine learning
· for large-scale modeling
· for data preparation and feature engineering
· for variable selection and model selection
Submission Instructions
Full-length papers (up to 10 page) or extended abstracts (2-4 pages) are requested and using ACM Proceedings Format (https://www.acm.org/publications/proceedings-template) is recommended.
All papers 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=automl2019
Important Dates
May 5, 2019: Due date for paper/abstract submissions
June 1, 2019: Notification of acceptance to authors
June 15, 2019: Camera-ready final submission of accepted papers
August 5, 2019: Workshop
Contact Us
For any questions, please email the organizing committee at ai.ml.automation@gmail.com
According to Forbes in December of 2018, one of the 5 Artificial Intelligence Trends To Watch Out For In 2019 is the gain in prominence of automated machine learning. The term AutoML is appearing more and more in data science discussions, publications, applications, and systems, as an aid to build better machine learning models. AutoML is being used in autonomous driving applications, sales forecasting and lead prioritization systems, and in many generic systems to generate and optimize machine learning pipelines that can select features, transform data, select the best model type and optimize hyperparameters. The debates continue regarding the level to which data science can and should be automated, the level of machine learning knowledge and expertise needed to build quality models, and the where and when manual intervention is necessary, yet the development and application of approaches and tools to automate repeated tasks continues to increase. The advancement, education, and adoption of data mining and machine learning practices require a transformation of theory to application, and feedback from application to theory. The development of tools to automate data mining efforts fosters this transformation and feedback and also promotes the development of standards and the adoption of these standards. Automated standards enable researchers and practitioners to better communicate, sharing successes and challenges in a more consistent common language. In an age of software as a service and ever-increasing scalability requirements, standards are necessary. Consistent adoption, application, and communication in turn promote research and refinement of the automated strategies and growth of the community. To keep pace with the rapidly increasing volume and rate of data generation, standardization and automating of data mining activities are critical. The challenges that must be discussed relate to the boundaries of automated tasks and individual attention needed for each unique business and data scenario.
This workshop will be held in Conjunction with KDD 2019.
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
• IEEE SMC Human Perception in Multimedia Computing
http://www.ieeesmc.org/technical-activities/human-machine-systems/human-perception-in-multimedia-computing
Call For Content
We request either full papers (up to 10 pages, 6 to 8 pages are recommended) or extended abstracts (2-4 pages) be submitted by May 5, 2019. Accepted papers and abstracts will be presented as oral presentations.
A Best Paper Award will be presented during the workshop.
Topics include (but are not limited to):
• Automation and optimization
• Hyperparameter autotuning of machine learning algorithms
• Internet of things (IoT) and automation
• Automation bias
• Automation misuse
• Automated methods:
· in machine learning, data mining, predictive analytics, and deep learning
· in knowledge discovery in databases
· in autonomous vehicles
· in machine learning pipelines and process flows of production systems
· in big data applications
· for monitoring and updating models
· to detect fake news
· for streaming data
· for interpretable machine learning
· for large-scale modeling
· for data preparation and feature engineering
· for variable selection and model selection
Submission Instructions
Full-length papers (up to 10 page) or extended abstracts (2-4 pages) are requested and using ACM Proceedings Format (https://www.acm.org/publications/proceedings-template) is recommended.
All papers 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=automl2019
Important Dates
May 5, 2019: Due date for paper/abstract submissions
June 1, 2019: Notification of acceptance to authors
June 15, 2019: Camera-ready final submission of accepted papers
August 5, 2019: Workshop
Contact Us
For any questions, please email the organizing committee at ai.ml.automation@gmail.com
Journal Promotion
|
| Acta Koreana (Acta Koreana) |
|
| Acta Via Serica (AVS) |
|
| Computers and Concrete (CAC) |
|
| Journal of Information Processing Systems (JIPS) |
|
| Journal of Internet Computing and Services (KSII - JICS) |
|
| Journal of Smart Tourism (Smart Tourism) |
|
| Membrane and Water Treatment (MWT) |
|
| Wind and Structures, An International Journal (Wind and Structures) |
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