Recent Advances in Automated Machine Learning: 2nd Edition

Dear Colleagues,

We are seeking submissions for a Special Issue entitled “Recent Advances in Automated Machine Learning”.

Big data, a phenomenon which has spurred remarkable advances in deep learning, can now be found in various domains, with many researchers investigating theories and applications of automated machine learning (AutoML). Advances in AutoML will have a huge impact in many areas of deep learning, such as data preparation, feature engineering, model selection and evaluation, hyperparameter tuning, network architecture search, and ensemble methods. For machine learning projects to be successful, we need to automate exploratory data analysis and feature selection to explore and understand the context, properties, and quality of the data. In this initial process, automated data exploration tools and feature recommendation tools will be of great assistance. For optimal performance in terms of learning time and evaluation metrics (including accuracy), however, we need to develop effective model selection and evaluation methods to search for optimal hyperparameters and network architectures. Moreover, since AutoML methodologies deal with multiple models simultaneously, we need to devise smart strategies for maintaining homogeneous/heterogeneous models with parallelized and limited resources. Techniques for searching for (or creating) optimal hyperparameters and network architectures with contemporary machine learning scenarios such as federated machine learning, meta-learning, self-supervised machine learning, etc., are attracting increasing interest from the research community.

For this Special Issue, we invite submissions that present cutting-edge research and the recent advances in the fields of automated machine learning. Both theoretical and experimental studies, as well as comprehensive review and survey papers, are welcome.

Prof. Dr. Dae-Ki Kang
Guest Editor

Manuscript Submission Information

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