Objective This work aims to provide a review of the existing literature in the field of
automated machine learning (AutoML) to help healthcare professionals better utilize …

Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

This paper presents an AI-guided approach to automatically discover low-carbon cost-
effective ultra-high performance concrete (UHPC). The presented approach automates data …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

With the continuous and vast increase in the amount of data in our digital world, it has been
acknowledged that the number of knowledgeable data scientists can not scale to address …

We study the problem of using low computational cost to automate the choices of learners
and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of …

Graph structured data are abundant in the real world. Among different graph types, directed
acyclic graphs (DAGs) are of particular interest to machine learning researchers, as many …

Modern systems can augment people's capabilities by using machine-learned models to
surface intelligent behaviors. Unfortunately, building these models remains challenging and …

Algorithm selection and hyperparameter tuning remain two of the most challenging tasks in
machine learning. Automated machine learning (AutoML) seeks to automate these tasks to …