Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

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 Machine learning (ML) has become a vital part in many aspects of our daily life.
However, building well performing machine learning applications requires highly …

The success of machine learning in a broad range of applications has led to an ever-
growing demand for machine learning systems that can be used off the shelf by non-experts …

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 …

Comparing different AutoML frameworks is notoriously challenging and often done
incorrectly. We introduce an open and extensible benchmark that follows best practices and …

Machine learning (ML) has been slowly entering every aspect of our lives and its positive
impact has been astonishing. To accelerate embedding ML in more applications and …

Something fundamental to computation-based research has really changed in the last ten
years. In certain fields, progress is simply dramatically more rapid than previously …

Automated machine learning (AutoML) supports the algorithmic construction and data-
specific customization of machine learning pipelines, including the selection, combination …