Explainable and Reproducible Machine Learning
Description
As machine learning is being applied in an ever increasing number of real-life scenarios, it is mandatory to design methods that are stable and provide reproducible results. Further, in many contexts, models should not only be predictive but also provide explainable and interpretable solutions. This is of particular importance in the common setting in which data are extremely sparse or heterogeneous. We work towards these goals using sparse models as well as integrating data modalities to capture complex interactions while ensuring explainability.