AutoML: Advances and Applications

Dear Colleagues,

The proliferation of machine learning in a myriad of domains has allowed researchers and practioners to harness its potential in scientific research beyond the immediate purview of computer science, such as drug interaction prediction, traffic pattern prediction, image recognition and natural language processing in medical or legal texts.

Applications of ML, however, come with the tasks of (i) formulating abstract problem in terms of ML, (ii) obtaining knowledge to select the right ML model with the correct hperparameters, (iii) preparing the data, (iv) training the models, and (v) extracting the output. Automated machine learning or AutoML lowers the barrier to utilizing ML for research purposes by a wider audience [1], eliminating the need to have AI/ML experts to  use advanced ML or deep learning models.

AutoML provides methods and processes to make ML available for non-ML experts, to improve the efficiency and efficacy of ML and to democratize ML. The AutoML literature includes, but is not limited to, the folowing broad topics: (i) hyperparameter optimization, (ii) neural architecture searching, (iii) ML model selection, (iv) transfer learning, (v) data preprocessing, and (vi) postprocess ML models.

There are openly available packages and services that allow for AutoML to be used such as AutoGluon, AutoSklearn and AutoWEKA.

1. Hutter, Frank, Lars Kotthoff, and Joaquin Vanschoren. Automated machine learning: methods, systems, challenges. Springer Nature, 2019.
2. He, X., Zhao, K., & Chu, X. (2021). AutoML: A survey of the state-of-the-art. Knowledge-Based Systems, 212, 106622.

Dr. Gerald Friedland
Dr. Debanjan Datta
Guest Editors

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