Connectionists: ECAI-14 Workshop and Tutorial on Metalearning & Algorithm Selection

[Pavel Brazdil]

MetaSel - Meta-learning & Algorithm Selection
*********************************************
ECAI-2014 Workshop, Prague,
19 August 2014 (date altered)
http://metasel2014.inescporto.pt/

Announcement & Call for Papers

Objectives
This ECAI-2014 workshop will provide a platform for discussing the
nature of algorithm selection which arises in many diverse domains,
such as machine learning, data mining, optimization and satisfiability
solving, among many others.

Algorithm Selection and configuration are increasingly relevant today.
Researchers and practitioners from all branches of science and
technology face a large choice of parameterized machine learning
algorithms, with little guidance as to which techniques to use.
Moreover, data mining challenges frequently remind us that algorithm
selection and configuration are crucial in order to achieve the best
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 and prior experiments. We also
discuss the prerequisites for effective meta-learning systems such as
recent infrastructure such as OpenML.org.

Many problems of today require that solutions be elaborated in the
form of complex systems or workflows which include many different
processes or operations. Constructing such complex systems or
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.

The workshop will include invited talks, presentations of
peer-reviewed papers and panels. The invited talks will be by Lars
Kotthoff and Frank Hutter (to be confirmed).

The target audience of this workshop includes researchers (Ph.D.'s)
and research students interested to exchange their knowledge about:
    - problems and solutions of algorithm selection and algorithm configuration
    - how to use software and platforms to select algorithms in practice
    - how to provide advice to end users about which algorithms to
select in diverse domains, including optimization, SAT etc. and
incorporate this knowledge in new platforms.

We specifically aim to attract researchers in diverse areas that have
encountered the problem of algorithm selection and thus promote
exchange of ideas and possible collaborations.

Topics
    Algorithm Selection & Configuration
    Planning to learn and construct workflows
    Applications of workflow planning
    Meta-learning and exploitation of meta-knowledge
    Exploitation of ontologies of tasks and methods
    Exploitation of benchmarks and experimentation
    Representation of learning goals and states in learning
    Control and coordination of learning processes
    Meta-reasoning
    Experimentation and evaluation of learning processes
    Layered learning
    Multi-task and transfer learning
    Learning to learn
    Intelligent design
    Performance modeling
    Process mining

Submissions and Review Process
Important dates:
    Submission deadline: 25 May 2014
    Notification: 23 June 2014

Full papers can consist of a maximum of 8 pages, extended abstracts up
to 2 pages, in the ECAI format. Each submission must be submitted
online via the Easychair submission interface.

Submissions can be updated at will before the submission deadline.
Electronic versions of accepted submissions will also be made publicly
available on the conference web site. The only accepted format for
submitted papers is PDF.

Submissions are possible either as a full paper or as an extended
abstract. Full papers should present more advanced work, covering
research or a case application. Extended abstracts may present
current, recently published or future research, and can cover a wider
scope. For instance, they may be position statements, offer a specific
scientific or business problem to be solved by machine learning (ML) /
data mining (DM) or describe ML / DM demo or installation.

Each paper submission will be evaluated on the basis of relevance,
significance of contribution, technical quality, scholarship, and
quality of presentation, by at least two members of the program
committee. All accepted submissions will be included in the conference
proceedings. At least one author of each accepted full paper or
extended abstract is required to attend the workshop to present the
contribution.

A selection will be made of the best paper and runner ups, and these
will be presented in the plenary session. The remainder of accepted
submissions will be presented in the form of short talks and a poster
session. All accepted papers, including those presented as a poster,
will be published in the workshop proceedings (possibly as CEUR
Workshop Proceedings). The papers selected for plenary presentation
will be identified in the proceedings.

Organizers:
    Pavel Brazdil, FEP, Univ. of Porto / Inesc Tec, Portugal, pbrazdil
at inescporto.pt
    Carlos Soares, FEUP, Univ. of Porto / Inesc Tec, Portugal, csoares
at fe.up.pt
    Joaquin Vanschoren, Eindhoven University of Technology (TU/e), Eindhoven,
       The Netherlands, j.vanschoren at tue.nl
    Lars Kotthoff, University College Cork, Cork, Ireland, larsko at 4c.ucc.ie

Program Committee:
    Pavel Brazdil, LIAAD-INESC Porto L.A. / FEP, University of Porto, Portugal
    André C. P. Carvalho, USP, Brasil
    Claudia Diamantini, Università Politecnica delle Marche, Italy
    Johannes Fuernkranz, TU Darmstadt, Germany
    Christophe Giraud-Carrier, Brigham Young Univ., USA
    Krzysztof Grabczewski, Nicolaus Copernicus University, Poland
    Melanie Hilario, Switzerland
    Frank Hutter, University of Freiburg, Germany
    Christopher Jefferson, University of St Andrews, UK
    Alexandros Kalousis, U Geneva, Switzerland
    Jörg-Uwe Kietz, U.Zurich, Switzerland
    Lars Kotthoff, University College Cork, Ireland
    Yuri Malitsky, University College Cork, Ireland
    Bernhard Pfahringer, U Waikato, New Zealand
    Vid Podpecan, Jozef Stefan Institute, Slovenia
    Ricardo Prudêncio, Univ. Federal de Pernambuco Recife (PE), Brasil
    Carlos Soares, FEP, University of Porto, Portugal
    Guido Tack, Monash University, Australia
    Joaquin Vanschoren, U. Leiden / KU Leuven
    Ricardo Vilalta, University of Houston, USA
    Filip Zelezný, CVUT, Prague, R.Checa

Previous events
This workshop is closely related to the PlanLearn-2012, which took
place at ECAI-2012 and other predecessor workshops in this series.


Tutorial on Metalearning and Algorithm Selection at ECAI-2014
*************************************************************
18 August 2014
http://metasel.inescporto.pt/

Algorithm Selection and configuration are increasingly relevant today.
Researchers and practitioners from all branches of science and
technology face a large choice of parameterized algorithms, with
little

guidance as to which techniques to use. Moreover, data mining
challenges frequently remind us that algorithm selection and
configuration are crucial in order to achieve the best 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 a limited time.
In this tutorial, we elucidate the nature of algorithm selection and
how it arises in many diverse domains, such as machine learning, data
mining, optimization and SAT solving. We show that it is possible to
use meta-learning techniques to identify the potentially best
algorithm(s) for a new task, based on meta-level information and prior
experiments. We also discuss the prerequisites for effective
meta-learning systems, and how recent infrastructures, such as
OpenML.org, allow us to build systems that effectively advice users on
which algorithms to apply.

The intended audience includes researchers (Ph.D.'s), research
students and practitioners interested to learn about, or consolidate
their knowledge about the state-of-the-art in algorithm selection and
algorithm configuration, how to use Data Mining software and platforms
to select algorithms in practice, how to provide advice to end users
about which algorithms to select in diverse domains, including
optimization, SAT etc. and incorporate this knowledge in new
platforms. The participants should bring their own laptops.