38 citations · 195 across the 35 of their papers we have counts for
15 papers · 1 filter
Analysis of Network Lasso for Semi-Supervised Regression
A. Jung, N. Vesselinova
We apply network Lasso to semi-supervised regression problems involving network structured data. This approach lends quite naturally to highly scalable learning algorithms in the f…
On The Complexity of Sparse Label Propagation
Alexander Jung
This paper investigates the computational complexity of sparse label propagation which has been proposed recently for processing network structured data. Sparse label propagation a…
Graph Signal Sampling via Reinforcement Learning
Oleksii Abramenko, Alexander Jung
We formulate the problem of sampling and recovering clustered graph signal as a multi-armed bandit (MAB) problem. This formulation lends naturally to learning sampling strategies u…
Recovery Conditions and Sampling Strategies for Network Lasso
Alexandru Mara, Alexander Jung
The network Lasso is a recently proposed convex optimization method for machine learning from massive network structured datasets, i.e., big data over networks. It is a variant of…
Learning to Avoid Errors in GANs by Manipulating Input Spaces
Alexander B. Jung
Despite recent advances, large scale visual artifacts are still a common occurrence in images generated by GANs. Previous work has focused on improving the generator's capability t…
A Fixed-Point of View on Gradient Methods for Big Data
Alexander Jung
Interpreting gradient methods as fixed-point iterations, we provide a detailed analysis of those methods for minimizing convex objective functions. Due to their conceptual and algo…