Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …

Several algorithms involving the Variational Rényi (VR) bound have been proposed to
minimize an alpha-divergence between a target posterior distribution and a variational …

Standard Bayesian learning is known to have suboptimal generalization capabilities under
misspecification and in the presence of outliers. Probably approximately correct (PAC) …

This paper focuses on $\alpha $-divergence minimisation methods for Variational Inference.
More precisely, we are interested in algorithms optimising the mixture weights of any given …

Bayesian uncertainty quantification is the key element to many machine learning
applications. To this end, approximate inference algorithms are developed to perform …

Artificial intelligence is widely viewed as a key enabler of sixth generation wireless systems.
In this thesis we target fundamental problems arising from the interaction between these two …

Probabilistic modeling, as known as probabilistic machine learning, provides a principled
framework for learning from data, with the key advantage of offering rigorous solutions for …