Machine Learning for Big Data
Research
Our research revolves around machine learning models and methods for big data over networks. The data arising in many important big data applications, ranging from social networks to network medicine, consist of high-dimensional data points related by an intrinsic (complex) network structure. In order to jointly leverage the information conveyed in the network structure as well as the statistical power contained in high-dimensional data points, we study networked exponential families. For the accurate learning of such networked exponential families, we borrow statistical strength, via the intrinsic network structure, across the dataset. A powerful algorithmic toolbox for designing learning algorithms is provided by convex optimization methods. Modern convex optimization methods are appealing for big data applications as they can be implemented as highly scalable message passing protocols.
Latest publications
Clustering students’ text form feedback data: comparison of eight vector space models
Federated Learning : From Theory to Practice
Engineering Trustworthy AI : A Developer Guide for Empirical Risk Minimization
Analysis of Total Variation Minimization for Clustered Federated Learning
Maschinelles Lernen: Die Grundlagen
The Aalto Dictionary of Machine Learning
Engineering Trustworthy AI : A Developer Guide for Empirical Risk Minimization
From intangible to tangible : The role of big data and machine learning in walkability studies
Explainable empirical risk minimization
Hercules : Deep Hierarchical Attentive Multi-Level Fusion Model with Uncertainty Quantification for Medical Image Classification
People
Henrik Ambos
Laia Amorós Carafí
