38 citations · 195 across the 35 of their papers we have counts for
8 papers · 1 filter
When is Network Lasso Accurate: The Vector Case
Nguyen Tran, Saeed Basirian, Alexander Jung
A recently proposed learning algorithm for massive network-structured data sets (big data over networks) is the network Lasso (nLasso), which extends the well- known Lasso estimato…
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…
The Network Nullspace Property for Compressed Sensing of Big Data over Networks
Alexander Jung, Madelon Hulsebos
We present a novel condition, which we term the net- work nullspace property, which ensures accurate recovery of graph signals representing massive network-structured datasets from…
Random Walk Sampling for Big Data over Networks
Saeed Basirian, Alexander Jung
It has been shown recently that graph signals with small total variation can be accurately recovered from only few samples if the sampling set satisfies a certain condition, referr…