Monday 23rd September 2013
- Scalable Decision Making
- Music and Machine Learning
- Reinforcement Learning with Generalized Feedback
- Languages for Data Mining and Machine Learning
- Data Mining on Linked Data
- Mining Ubiquitous and Social Environments
- Data Analytics for Renewable Energy Integration
Friday 27th September 2013
- Discovery Challenge Workshop
- Tensor Methods in Machine Learning
- Solving Complex Machine Learning Problems with Ensemble Methods
- Sports Analytics
- New Frontiers in Mining Complex Patterns
- Real-World Challenges for Data Stream Mining
Some Workshops have extended the paper submission deadline. More information on the websites of the Workshops.
|Workshops takes place:||September 23, 2013 and September 27, 2013|
Details about the submission process for each workshop can be found at the corresponding website.
Monday Workshops (23rd September 2013)
SCALE: Scalable Decision Making: Uncertainty, Imperfection, Deliberation.
Tatiana V. Guy, Miroslav Kárný
Machine learning and knowledge discovery both use and serve to decision making, which faces uncertainty, incomplete knowledge, problem and data complexity, and imperfection (limited cognitive and evaluating capabilities) of the heterogeneous multiple participants (aka agents, decision makers, components, controllers, classifiers, etc.). Without a moderator, such decision making lacks a firm prescriptive basis and this repeatedly emerging state has no easy solution. While the theoretical, algorithmic and application achievements are immense, real-life complex problems uncover discrepancies between normative and descriptive theories. The workshop aims to exploit the knowledge and experience of multi-disciplinary community and to extract a set of fundamental concepts describing a phenomenon of dynamic decision making with interacting imperfect selfish participants: an abstraction of locally acting but interacting robots, computer algorithms, controllers, experts, etc. as well as their combinations. The targeted audience covers the machine learning, knowledge discovery and artificial intelligence communities and the researchers from the communities that deal with decision, cognitive, social and natural sciences, engineering, complex systems, biology, economy, mathematics and physics.
MML: Machine Learning and Music
Rafael Ramirez, Darrell Conklin and José Manuel Iñesta
With the current explosion and quick expansion of music in digital formats, and the computational power of modern systems, the research on machine learning and music has gained increasing popularity. As complexity of the problems investigated by researchers on machine learning and music increases, there is a need to develop new algorithms and methods to solve these problems. Machine learning has proved to provide efficient solutions to many music-related problems both of academic and commercial interest. MML 2013 concentrates around the topic of “Intelligent Content-Based Music Processing” and will welcome contributions describing machine learning approaches in this context, e.g. automatic classification of music (audio and MIDI), style-based interpreter recognition, automatic composition and improvisation, music recommender systems, and expressive performance modeling.
PBRL: Reinforcement Learning with Generalized Feedback: Beyond Numeric Rewards
Johannes Fuernkranz, Eyke Hüllermeier
In recent years, different generalizations of the standard setting of reinforcement learning have emerged; in particular, several attempts have been made to relax the quite restrictive requirement for numeric feedback and to learn from different types of more flexible training information. Examples of generalized settings of that kind include learning from expert demonstration (e.g., apprenticeship learning or inverse reinforcement learning), learning from qualitative feedback (e.g., ordinal MDPs or preference-based reinforcement learning), and learning from multiple feedback signals (e.g., multi-objective reinforcement learning). These extensions and variants of reinforcement learning are closely connected and largely intersecting with preference learning, a new subfield of machine learning that
deals with the learning of (predictive) preference models from observed/revealed or automatically extracted preference information. The goal of this workshop is to help in unifying and streamlining research on generalizations of standard reinforcement learning, which, for the time being, seem to be pursued independently. Ideally, the workshop will help the participants to identify some common ground of their work, thereby helping the field move toward a theoretical foundation of reinforcement learning with generalized feedback.
LML: Languages for Data Mining and Machine Learning
Bruno Crémilleux, Luc De Raedt, Paolo Frasconi, Tias Guns
The workshop aims to bring together researchers and stimulate discussions on languages for data mining and machine learning. Its main motivation is the believe that designing generic and declarative modeling languages for data mining and machine learning, together with efficient solving techniques, is an attractive direction that can boost scientific progress.
DMoLD: Data Mining on Linked Data
Claudia d’Amato, Petr Berka, Vojtěch Svátek, Krzysztof Węcel
Linked data, published on the web in RDF format, represents a novel type of data source that has been so far nearly untouched by advanced data mining methods. Its unique features, compared to traditional datasets, are the heterogeneous provenance of individual interconnected resources, their varying degree and form of structuredness, and their grounding in numerous semantic vocabularies. The workshop features both an open track, for papers on applying any kind of data mining methods to linked data sources, and a Linked Data Mining Challenge, with two predictive tasks and one exploratory one, all in the domain of public procurement.
MUSE: Mining Ubiquitous and Social Environments
Martin Atzmueller, Christoph Scholz
The goal of this workshop is to promote an interdisciplinary forum for researchers working in the fields of ubiquitous computing, mobile sensing, social web, Web 2.0, and social networks which are interested in utilizing data mining in those contexts. The workshop seeks for contributions adopting state-of-the-art mining algorithms on ubiquitous social data. Papers combining aspects of the two fields are especially welcome. In short, we want to accelerate the process of identifying the power of advanced data mining operating on data collected in ubiquitous and social environments, as well as the process of advancing data mining through lessons learned in analyzing these new data.
DARE: Data Analytics for Renewable Energy Integration
Stuart Madnick, Wei Lee Woon, Zeyar Aung
Climate change, the depletion of natural resources and rising energy costs have led to an increasing focus on renewable sources of energy. A lot of research has been devoted to the technologies used to extract energy from these sources; however, equally important is the storage and distribution of this energy in a way that is efficient and cost effective. Achieving this would generally require integration with existing energy infrastructure. The challenge of renewable energy integration is inherently multidisciplinary and is particularly dependant on the use of techniques from the domains of data analytics, pattern recognition and machine learning. Examples of relevant research topics include the forecasting of electricity supply and demand, the detection of faults, demand response applications and many others. This workshop will provides a forum where interested researchers from the various related domains will be able to present and discuss their findings.
Friday Workshops (27rd September 2013)
Discovery Challenge Workshop
Stephan Doerfel, Andreas Hotho, Robert Jäschke, Folke Mitzlaff, Jürgen Müller
See http://www.ecmlpkdd2013.org/discovery-challenge/ for more details.
TML: Tensor Methods for Machine Learning
Maximilian Nickel, Volker Tresp
Tensors, as generalizations of vectors and matrices, have become increasingly popular in different areas of machine learning and data mining, where they are employed to approach a diverse number of difficult learning and analysis tasks. Prominent examples include learning on multi-relational data and large-scale knowledge bases, recommendation systems, computer vision, mining boolean data, neuroimaging or the analysis of time-varying networks. The success of tensor methods is strongly related to their ability to efficiently model, analyse and predict data with multiple modalities. To address specific challenges and problems, a variety of methods has been developed in different fields of application. This workshop should serve as a basis for an interdisciplinary exchange of methods, ideas and techniques, with the goal to develop a deeper understanding of tensor methods in machine learning, further advance existing approaches and enable new approaches to important problems. The workshop is intended for researchers in the machine learning, data mining and tensor communities to discuss novel methods and applications as well as theoretical advances.
COPEM: Solving Complex Machine Learning problems with Ensemble Methods
Ioannis Katakis, Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Ioannis Partalas
Ensemble methods are widely utilized within the machine learning community due to their accuracy-improving and robustness attributes. Since even elementary ensemble approaches outperform single learners, multiple classifier systems are the go-to solution in applications where higher predictive performance is required. The emphasis in COPEM is to discuss ensemble strategies that solve difficult machine learning tasks. This workshop will bring together the ensemble method community and researchers that are not ensemble-experts but could benefit from the use of such techniques to confront interesting research challenges. The goals of COPEM are: a) to discuss state-of-the-art approaches that exploit ensembles to solve complex machine learning problems and, b) to bring the community together and discuss interesting future applications. The ultimate objective of COPEM is not only to present high quality research papers but, more importantly, to dynamically initiate new collaborations that will work towards new challenges. Following this direction, the workshop will feature networking activities.
MLSA: (Machine Learning and Data Mining for) Sports Analytics
Albrecht Zimmermann, Jan van Haaren, Jesse Davis
The Machine Learning and Data Mining for Sports Analytics workshop at ECML/PKDD 2013 solicits papers on Machine Learning, Data Mining, and other related approaches for sports analytics. The application of analytic techniques is rapidly gaining traction in both professional and amateur sports circles. The majority of techniques used in the field so far are statistical. While there has been some interest in the Machine Learning and Data Mining community, it has been somewhat muted so far. The goal of this workshop is two-fold. The first is to raise awareness about this emerging application area. The second is bring members of the sport analytics community into contact with typical ECML/PKDD contributors, and to highlight what the community has done and can do in the field.
NFMCP: New Frontiers on Mining Complex Patterns
Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras
Data mining and knowledge discovery can be considered today as mature research fields with numerous algorithms and studies to extract knowledge from data in different forms. Although, most existing data mining approaches look for patterns in tabular data, there are also numerous studies which already look for patterns in complex data (e.g. multi-table data, XML data, web data, time series and sequences, graphs and trees). The recent developments in technologies and life sciences have paved the way to the proliferation of data collections representing new complex interactions between entities in distributed and heterogeneous sources. These interactions may be spanned at multiple levels of granularity as well as at spatial and temporal dimensions. The purpose of this workshop is to bring together researchers and practitioners of data mining interested in exploring emerging technologies and applications where complex patterns in expressive languages are principally extracted from new prominent data sources like blogs, event or log data, biological data, spatio-temporal data, social networks, mobility data, sensor data and streams, and so on. We are interested in advanced techniques which preserve the informative richness of data and allow us to efficiently and efficaciously identify complex information units present in such data.
RealStream: Real-World Challenges for Data Stream Mining
Georg Krempl, Indrė Žliobaitė, Yin Wang, George Forman
This workshop will provide a forum for researchers and practitioners to discuss real-world challenges for data stream mining, identify gaps between data streams research and meaningful applications, and define new application-relevant research directions for data stream mining. The workshop will focus on oral presentations and discussions. Submissions of extended abstracts (up to 4 pages) are invited.