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
Hercules
Towards Model-Agnostic Federated Learning over Networks
Clustered Federated Learning via Generalized Total Variation Minimization
Wind to start the dishwasher? High-Resolution Wind Atlas for Finland
Wind to start the washing machine? High-Resolution Wind Atlas for Finland
Machine Learning
Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams
From Intuition to Reasoning
Images of Galois representations in mod p Hecke algebras
Learning optimal lattice codes for MIMO communications
People

Henrik Ambos

Laia Amorós Carafí

Timo Huuhtanen
Arttu Mäkinen
Roope Tervo
Nguyen Tran
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