There have been many debates in recent years about the need and the ability to automate data mining and machine learning tasks. A recent blog post titled “Data Scientists Need More Automation” discusses the repeated efforts required to configure and run services or scripts on a network of machines. Other discussions ask, “Can We Automate Data Mining?,” arguing that many tasks performed by data scientists “cannot be automated and need manual intervention”; in other words, expertise is needed for each individual case, requiring clear understanding of the business and the data. 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 the 2018 IEEE International Conference on Big Data.
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
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 October 10, 2018. Accepted papers and abstracts will be presented as oral presentations. Full papers will be included in the IEEE BigData 2018 conference proceedings.
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
Papers or extended abstracts should be submitted from the following site:
Oct 10, 2018: Due date for abstract submissions
Nov 1, 2018: Notification of acceptance to authors
Nov 15, 2018: Camera-ready of accepted papers
Dec 10-13, 2018: Workshop
The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data.
• The first conference IEEE Big Data 2013 ( https://ift.tt/O9sqGB regular paper acceptance rate: 17.0%)was held in Santa Clara, CA from Oct 6-9, 2013 with more than 400 registered participants from 40 countries.
• The IEEE Big Data 2014 (https://ift.tt/PVcWqD, regular paper acceptance rate: 18.5.0%) was held in Washington DC, Oct 27-30, 2014 with more than 600 registered participants from 45 countries.
• The IEEE Big Data 2015 ( https://ift.tt/1BrcVQs , regular paper acceptance rate: 16.8%) was held in Santa Clara, Oct29-Nov 1, 2015 with more than 780 registered participants from 49 countries.
• The IEEE Big Data 2016 ( https://ift.tt/1UxAMET , regular paper acceptance rate: 18.7%) was held in Washington DC, Dec 5-8, 2016 with close to 900 registered participants from 43 countries.
• The IEEE Big Data 2017 ( https://ift.tt/2niqCR7 , regular paper acceptance rate: 17.8%) was held in Boston, MA, Dec 11-14, 2017 with close to 1000 registered participants from 50 countries.
For any questions, please email the organizing committee at firstname.lastname@example.org
from CFPs on Artificial Intelligence : WikiCFP https://ift.tt/2t2LM6c