[dbitaly] CFP for AutoML workshop at ECML/PKDD-2017

Claudia Diamantini c.diamantini a univpm.it
Gio 8 Giu 2017 19:30:08 CEST


CALL FOR PAPERS

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 a automl.org <mailto:ecmlpkdd2017 a 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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