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 …

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 …

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

Increasingly larger number of software systems today are including data science
components for descriptive, predictive, and prescriptive analytics. The collection of data …

Machine learning research depends on objectively interpretable, comparable, and
reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites …

Automated Machine Learning (AutoML) systems are emerging that automatically search for
possible solutions from a large space of possible kinds of models. Although fully automated …

While the demand for machine learning (ML) applications is booming, there is a scarcity of
data scientists capable of building such models. Automatic machine learning (AutoML) …

Automated machine learning (AutoML) has achieved remarkable progress on various tasks,
which is attributed to its minimal involvement of manual feature and model designs …

Scientific data analyses often combine several computational tools in automated pipelines,
or workflows. Thousands of such workflows have been used in the life sciences, though their …

Automated machine learning (AutoML) aims for constructing machine learning (ML)
pipelines automatically. Many studies have investigated efficient methods for algorithm …