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 tensors 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. A particular focus of this workshop is to uncover underlying principles in tensor methods, their applications and associated problems. The workshop is intended for researchers in the machine learning, data minining and tensor communities to discuss novel methods and applications as well as theoretical advances.
The workshop consists of contributed talks, poster sessions and a number of invited talks which will cover important work and recents developments in tensor methods. Furthermore, the workshop will include open discussion sessions to encourage the exchange of ideas and the development of a common understanding of problems and methods among the participants of the workshop.