The ECML-PKDD 2017 Workshop on Automatic Machine Learning (AutoML)
Collocated with ECML-PKDD in Skopje, Macedonia, September 22, 2017
Web: http://ecmlpkdd2017.automl.org
Email: ecmlpkdd2017@automl.org
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Important Dates:
Submission deadline: 10 July, 2017, 11:59pm UTC-12 (July 10 anywhere in
the world)
Notification: 30 July, 2017
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AutoML: Automatic selection, configuration and composition of machine
learning algorithms
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
- Reinforcement learning for parameter control & algorithm design
- 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
Co-chairs: Frank Hutter, Holger Hoos, Pavel Brazdil and Joaquin Vanschoren
We welcome standard submissions of up to 6 pages (not including references)
in ECML-PKDD format, as well as longer papers of up to 15 pages (not
including references).
For further details, please see the submission page; the submission
deadline is July 10th, 2017.
All accepted papers will be presented as posters and very short poster
spotlights; the best paper(s) will be selected for an oral presentation.
At least one author of each accepted paper should be registered for the
main conference.
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