One of the important tasks of modern machine learning is extracting the structural and temporal information from datasets. For example, we may want to analyse the time-evolving friendship network structure from social network data. New statistical tools are being created to study and exploit this dynamic nature of various applications.
In contrast to a classic, stationary view of machine learning datasets, this new angle allows us to enrich our understanding of the structural information in a changing system. Challenges arise in this domain together with new research opportunities from both theoretical and practical aspects.
Join this workshop to hear leading statistical machine learning experts talking about their own projects on learning from dynamic and changing data.
Registration is essential, as places are limited.