Abstract:
Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this tutorial, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research.

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Abstract:
The problem of formalizing interestingness of data mining results remains an important challenge in data mining research and practice. While it is widely recognized as a challenge in frequent pattern mining, in this tutorial we will explain that it also manifests itself in other data mining tasks such as dimensionality reduction, graph mining, clustering, and more. This tutorial aims to introduce the audience to a relatively new framework for addressing these challenges in a rigorous and generic manner. This framework is the result of the ERC project FORSIED (Formalizing Subjective Interestingness in Exploratory Data Mining), which has by now resulted in a body of work of sufficient maturity to make a well-rounded and useful tutorial possible, targeted at both colleague researchers as well as practitioners.

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Abstract:
In the last years, learning to rank (LtR) had a significant influence on several data mining tasks and in particular in the Information Retrieval field, with large research efforts coming both from the academia and the industry. Indeed, efficiency requirements must be fulfilled in order to make an effective research product deployable within an industrial environment. The evaluation of a model can be too expensive due to its size, the features used and several other factors. This tutorial discusses the recent solutions that allow to build an effective ranking model that satisfies temporal budget constrains at evaluation time.

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Abstract:
In this tutorial, we will present a concise and intuitive overview of both fundamental modeling principles of human mobility, and machine learning models applicable to specific mobility-related problems. In particular, we will review the state of the art of four main aspects in human mobility: (1) the human mobility data landscape; (2) key metrics and measures; (3) generative and phenomenological models at the level of individual, population and mixture of the two; (4) machine learning models for next location prediction.

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Abstract:
The tutorial will provide an in-depth introduction to the emerging field of deep learning for graphs and to its applications to network data analysis, to bioinformatics, chemistry, physics and vision. Dealing with graph data requires learning models capable of adapting to structured samples of varying size and topology, capturing the relevant structural patterns to perform predictive and explorative tasks while maintaining the efficiency and scalability necessary to process large scale networks. This tutorial will offer a totally novel perspective on this emerging topic, integrating the latest cutting edge research with foundational works dating back to over 20 years ago and whose knowledge and understanding is currently not widely diffused in the community. The tutorial is targeted to both early career researchers seeking ideas for their doctoral studies as well as to more advanced stage researchers looking to enter into a lively field of deep learning and seeking both foundational knowledge as well as a perspective on current research.

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Abstract:
Deep neural networks have recently broken records on a range of natural language tasks (e.g., speech recognition, machine translation). While there are long-standing methodological traditions pre-dating the modern wave of deep learning approaches, the impact of deep learning has been similarly positive on tasks like sentiment analysis and emotion detection. In this tutorial, we will provide a comprehensive coverage of both traditional and deep learning methods for handling natural language sentiment and affect. We will also introduce machine learning methods targeting multilingual processing of these tasks, handling a wide host of languages (including European languages and languages of complex morphology). We will also refer the audience to the primary available resources for these tasks. Further, we will discuss existing challenges and emergent methods cutting across text classification tasks in general. In conclusion, we will overview and debate associated legislative and ethical issues.

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