Emerging trends in machine learning: Beyond conventional methods and data

Recently, new promising theoretical results, techniques, and methodologies have attracted the attention of many researchers and have allowed to broaden the range of applications in which machine learning can be effectively applied in order to extract useful and actionable information from the huge amount of heterogeneous data produced everyday by an increasingly digital world. Examples of these methods and problems are: learning under privacy and anonymity constraints, learning from structured, semi-structured, multi-modal (heterogeneous) data, constructive machine learning, reliable machine learning, learning to learn, mixing deep and structured learning, semantics-enabled recommender systems, re-producibility and interpretability in machine learning, human-in-the-loop, adversarial learning. The focus of this special session is to attract both solid contributions or preliminary results which show the potentiality and the limitations of new ideas, refinements, or contaminations between the different fields of machine learning and other fields of research in solving real world problems. Both theoretical and practical results are welcome to our special session.