AutoML 2019: The Third International Workshop on Automation in Machine Learning |
Website | https://sites.google.com/view/automl2019-workshop/home |
Submission link | https://easychair.org/conferences/?conf=automl2019 |
Submission deadline | May 12, 2019 |
Notification of acceptance | June 1, 2019 |
Camera-ready final submission of accepted papers | June 15, 2019 |
Workshop | August 5, 2019 |
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.
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
List of Topics
- Automation and optimization
- Hyperparameter autotuning of machine learning algorithms
- Internet of things (IoT) and automation
- Automation bias and misuse
- Automated methods:
- in machine learning, data mining, predictive analytics, and deep learning
- 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
- for data preparation and feature engineering
- for variable selection and model selection
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference.
Full-length papers (up to 10 pages) or extended abstracts (2-4 pages) are welcome 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
Invited Speakers
- Dr. Jun (Luke) Huan, Head, Big Data Lab, Baidu Research
- AutoDL: Automated Deep Learning for Open and Inclusive AI
- Professor Xia (Ben) Hu, Department of Computer Science & Engineering, Texas A&M University
- Auto-Keras: An Efficient Neural Architecture Search System
Venue
The workshop will be held in conjunction with the KDD2019 conference, in Anchorage, AK, August 4-8, 2019
Contact
All questions about submissions should be emailed to the organizing committee at ai.ml.automation@gmail.com