This workshop will provide a platform for discussing recent developments in the areas of meta-learning, algorithm selection and configuration, which arise in many diverse domains and are increasingly relevant today. Researchers and practitioners from all areas of science and technology face a large choice of parameterized machine learning algorithms, with little guidance as to which techniques to use in a given application context. Moreover, data mining challenges frequently remind us that algorithm selection and configuration are crucial in order to achieve cutting-edge performance, and drive industrial applications.

Meta-learning leverages knowledge of past algorithm applications to select the best techniques for future applications, and offers effective techniques that are superior to humans both in terms of the end result and especially in the time required to achieve it. In this workshop, we will discuss different ways of exploiting meta-learning techniques to identify the potentially best algorithm(s) for a new task, based on meta-level information, including prior experiments on both past datasets and the current one.

Many contemporary problems also require the use of complex workflows that consist of several processes or operations. Constructing such complex workflows requires extensive expertise, and could be greatly facilitated by leveraging planning, meta-learning and intelligent system design. This task is inherently interdisciplinary, as it builds on expertise in various areas of AI.

Main research areas of relevance to this workshop include, but are not limited to:

- Algorithm / model selection and configuration

- Meta-learning and exploitation of meta-knowledge

- Hyperparameter optimization

- Automatic generation and evaluation of learning processes / workflows

- Representation learning and automatic feature extraction / construction

- Automatic feature coding / transformation

- Automatic detection and handling skewed data or missing values

- Automatic acquisition of new data (active learning, experimental design)

- Usage of planners in the construction of workflows

- Representation of learning goals and states in learning

- Control and coordination of learning processes

- Meta-reasoning

- Layered learning

- Multi-task and transfer learning

- Learning to learn

- Intelligent experiment design

Keynote speaker: TBA

Co-chairs: Frank Hutter, Holger Hoos, Pavel Brazdil and Joaquin Vanschoren