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\documentclass[runningheads,a4paper]{llncs}

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\begin{document}

\mainmatter  % start of an individual contribution

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\title{Declarative modeling for machine learning and data mining}

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\titlerunning{Declarative modeling for machine learning and data mining}

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\author{Luc De Raedt}
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\authorrunning{Luc De Raedt}

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\institute{University of Leuven, Belgium\\
\email{luc.deraedt@cs.kuleuven.be}\\ 
\url{http://people.cs.kuleuven.be/~luc.deraedt/}
}

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\toctitle{Declarative modeling for machine learning and data mining}
\tocauthor{De Raedt, Luc}
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\section*{Abstract}

Despite the popularity of machine learning and data mining today, it
remains challenging to develop applications and software that
incorporates machine learning or data mining techniques. This is
because machine learning and data mining have focussed on developing
high-performance algorithms for solving particular tasks rather than
on developing general principles and techniques.  I propose to
alleviate these problems by applying the constraint programming
methodology to machine learning and data mining and to specify machine
learning and data mining problems as constraint satisfaction and
optimization problems. What is essential is that the user be provided
with a way to declaratively specify what the machine learning or data
mining problem is rather than having to outline how that solution
needs to be computed.  This corresponds to a model + solver-based
approach to machine learning and data mining, in which the user
specifies the problem in a high level modeling language and the system
automatically transforms such models into a format that can be used by
a solver to efficiently generate a solution.  This should be much
easier for the user than having to implement or adapt an algorithm
that computes a particular solution to a specific problem.  Throughout
the talk, I shall use illustrations from our work on constraint
programming for itemset mining and probabilistic programming.

\section*{Bio}

Luc De Raedt is a full professor (of research) at the University
of Leuven (KU Leuven) in the Department of Computer Science and a
former chair of Machine Learning at the Albert-Ludwigs-University in
Freiburg.  Luc De Raedt has been working in the areas of artificial
intelligence and computer science, especially on computational logic,
machine learning and data mining, probabilistic reasoning and
constraint programming and their applications in bio- and
chemoinformatics, vision and robotics, natural language processing,
and engineering.  His work has typically crossed boundaries between
different research areas, often working towards an integration of
their principles. He is well-known for his early work on inductive
logic programming (combining logic with learning). Since 2000, he has
been working towards a further integration of logical and relational
learning with probabilistic reasoning (statistical relational learning
and probabilistic programming) and on inductive querying in
databases. During the last three years he has been fascinated by the
possibility of combining constraint programming principles with data
mining and machine learning. He is currently coordinating a European
IST FET project in this area (ICON — Inductive Constraint Programming)
and is the program chair of the 20th European Conference on Artificial
Intelligence (Montpellier, 2012). He was a program co-chair of ICML
2005 and ECML/PKDD 2001.

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