Networked Exponential Families for Big Data over Networks

Date: November 25, 2019

Abstract: The data arising in many important applications consists of high-dimensional data points that are related by an intrinsic network structure. We introduce networked exponential families to jointly leverage the information in the topology as well as the high-dimensional attributes (features or labels) of networked data points. Networked exponential families provide a flexible probabilistic model for heterogeneous datasets with intrinsic network structure. These models can be learnt efficiently using network Lasso, which implicitly pools or clusters the data points according to the intrinsic network structure and the local likelihood. The resulting method can be formulated as a non-smooth convex optimization problem which we solve using a primal-dual splitting method.
This primal-dual method is appealing for big data applications as it can be implemented as a highly scalable message-passing algorithm.

Bio: Alexander Jung received the Diplom-Ingenieur and Dr. techn. degrees in electrical engineering/signal processing from Vienna University of Technology, Vienna, Austria, in 2008, and 2011, respectively. After Post-Doc stays at TU Vienna and ETH Zurich, he joined Aalto in 2015. He is currently assistant professor for machine learning within the Department of Computer Science at Aalto University. His research interests are in statistical signal processing and machine learning for big data with emphasis on sparse models as well as trade-offs between accuracy and computational complexity of learning algorithms. He has taught the main courses on machine learning and artificial intelligence attracting several thousands of students from and beyond Aalto.

Speaker: Alex Jung
Affiliation: Assistant Professor of Computer Science, Aalto University

Place of Seminar: Lecture Hall Exactum D122, University of Helsinki