In this work, we devise robust and efficient learning protocols for
orch...
With privacy legislation empowering users with the right to be forgotten...
Machine unlearning refers to the task of removing a subset of training d...
Federated Learning (FL) is a novel paradigm for the shared training of m...
In federated learning (FL), robust aggregation schemes have been develop...
Adversarial training is a computationally expensive task and hence searc...
Deep Generative Models (DGMs) allow users to synthesize data from comple...
Federated learning (FL) is one of the most important paradigms addressin...
Federated Learning (FL) is an approach to conduct machine learning witho...
Data science is labor-intensive and human experts are scarce but heavily...
The growing interest in both the automation of machine learning and deep...
Adversarial examples have become an indisputable threat to the security ...
We introduce a new Bayesian multi-class support vector machine by formul...
Deep Learning models are vulnerable to adversarial examples, i.e. images...
In a real-world setting, visual recognition systems can be brought to ma...
Generative Adversarial Networks (GANs) have become a widely popular fram...