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LMCE 2014 # First International Workshop on Learning over Multiple Contexts @ ECML 2014 Generalization and reuse of machine learning models over multiple contexts A workshop held in conjunction with the ECML PKDD 2014, Nancy, France, 15-19 September 2014 http://www.dsic.upv.es/~flip/LMCE2014/ ################################################################## === Call for Papers === Adaptive reuse of learnt knowledge is of critical importance in the majority of knowledge-intensive application areas, particularly when the context in which the learnt model operates can be expected to vary from training to deployment. In machine learning this has been studied, for example, in relation to variations in class and cost skew in (binary) classification, leading to the development of tools such as ROC analysis to adjust decision thresholds to operating conditions concerning class and cost skew. More recently, considerable effort has been devoted to research on transfer learning, domain adaptation, and related approaches. Given that the main business of predictive machine learning is to generalise from training to deployment, there is clearly scope for developing a general notion of operating context. Without such a notion, a model predicting sales in Prague for this week may perform poorly in Nancy for next Wednesday. The operating context has changed in terms of location as well as resolution. While a given predictive model may be sufficient and highly specialised for one particular operating context, it may not perform well in other contexts. If sufficient training data for the new context is available it might be feasible to retrain a new model; however, this is generally not a good use of resources, and one would expect it to be more cost-effective to learn one general, versatile model that effectively generalizes over multiple and possibly previously unseen contexts. The aim of this workshop is to bring together people working in areas related to versatile models and model reuse over multiple contexts. Given the advances made in recent years on specific approaches such as transfer learning, an attempt to start developing an overarching theory is now feasible and timely, and can be expected to generate considerable interest from the machine learning community. Papers are solicited in all areas relating to model reuse and model generalization including the following areas: * transfer learning * data shift and concept drift * domain adaptation * transductive learning * multi-task learning * ROC analysis and cost-sensitive learning * background knowledge * relational learning * context-aware applications * incomplete information, abduction * meta-learning === Important Dates === Submission: 20 June 2014 Notification: 11 July 2014 Final verion: 25 July 2014 === Program Committee === Chowdhury Farhan Ahmed, University of Strasbourg, France Charles Elkan, University of California - San Diego, USA Amaury Habrard, University Jean Monnet (UJM) of Saint-Etienne, France Meelis Kull, University of Bristol, UK Dragos Margineantu, Boeing Research, USA Weike Pan, Shenzhen University, China Joaquin Quiñonero, Facebook, USA María José Ramírez-Quintana, Universitat Politècnica de València, Spain Carlos Soares, University of Porto, Portugal Masashi Sugiyama, Tokyo Institute of Technology, Japan Bianca Zadrozny, Federal University of Fluminense, Brazil Huimin Zhao, University of Wisconsin-Milwaukee, USA === Organising Committee === Cèsar Ferri, Technical University of Valencia, Spain<[email protected]> Peter Flach, University of Bristol, UK<[email protected]> Nicolas Lachiche, University of Strasbourg, France<[email protected]> For more information visithttp://www.dsic.upv.es/~flip/LMCE2014/
