132 citations · 521 across the 32 of their papers we have counts for
5 papers · 1 filter
A simple defense against adversarial attacks on heatmap explanations
Laura Rieger, Lars Kai Hansen
With machine learning models being used for more sensitive applications, we rely on interpretability methods to prove that no discriminating attributes were used for classification…
Client Adaptation improves Federated Learning with Simulated Non-IID Clients
Laura Rieger, Rasmus M. Th. Høegh, Lars K. Hansen
We present a federated learning approach for learning a client adaptable, robust model when data is non-identically and non-independently distributed (non-IID) across clients. By s…
Probabilistic Decoupling of Labels in Classification
Jeppe Nørregaard, Lars Kai Hansen
In this paper we develop a principled, probabilistic, unified approach to non-standard classification tasks, such as semi-supervised, positive-unlabelled, multi-positive-unlabelled…
IROF: a low resource evaluation metric for explanation methods
Laura Rieger, Lars Kai Hansen
The adoption of machine learning in health care hinges on the transparency of the used algorithms, necessitating the need for explanation methods. However, despite a growing litera…
Robustness analytics to data heterogeneity in edge computing
Jia Qian, Lars Kai Hansen, Xenofon Fafoutis +2
Federated Learning is a framework that jointly trains a model \textit{with} complete knowledge on a remotely placed centralized server, but \textit{without} the requirement of acce…