Recent advances in center-based clustering continue to improve upon the drawbacks of
Lloyd's celebrated $ k $-means algorithm over $60 $ years after its introduction. Various …

Recently, federated Learning (FL) has been widely used to protect clients' data privacy in
distributed applications, and heterogeneous data and model poisoning are two critical …

The latent space of pre-trained generative adversarial networks (GANs) is rich in semantic
information, which often becomes highly entangled. It is crucial to identify semantic …

Intrusion detection systems play a crucial role in ensuring the safety of vehicle driving.
Traditional intrusion detection systems face challenges in efficiently extracting key features …

In massive multiple-input multiple-output (MIMO) systems, block diagonalization-based
precoding methods are employed to mitigate interference among users by relying on …

This paper considers the problem of supervised learning with linear methods when both
features and labels can be corrupted, either in the form of heavy tailed data and/or corrupted …

Federated learning (FL) is a new machine learning paradigm to collaboratively learn an
intelligent model across many clients without uploading local data to the server. Non-IID …

Context The biodiversity faces an underlying threat from landscape fragmentation resulting
from rapid urbanization. Examining the future trajectory of landscape fragmentation is …

The growth in network technologies is attracting more and more cyber threats, and intrusion
detection systems are playing a main role in facing them, however with the size and …

The problem of linear predictions has been extensively studied for the past century under
pretty generalized frameworks. Recent advances in the robust statistics literature allow us to …