Editors’ Introduction

Abstract

The ALT-conference series is dedicated to studies on learning from an algorithmic and mathematical perspective. In the following, the five invited lectures and the regular contributions are introduced in some more detail.

Invited Talks. It is now a tradition of the co-located conferences ALT and DS to have a joint invited speaker–namely this year, Luc De Raedt. Since 2006 he is a full research professor at the Department of Computer Science of the Katholieke Universiteit Leuven (Belgium). His research interests are in artificial intelligence, machine learning and data mining, as well as their applications. He is currently working on probabilistic logic learning (sometimes called statistical relational learning), which combines probabilistic reasoning methods with logical representations and machine learning, the integration of constraint programming with data mining and machine learning principles, the development of programming languages for machine learning, and analyzing graph and network data. In his talk Declarative Modeling for Machine Learning and Data Mining he notes that 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 focused on developing high-performance algorithms for solving particular tasks rather than on developing general principles and techniques. He thus proposes 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. The aim 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.