3 papers
cs.LG2022
A piece-wise constant approximation for non-conjugate Gaussian Process models
Sarem Seitz
Gaussian Processes (GPs) are a versatile and popular method in Bayesian Machine Learning. A common modification are Sparse Variational Gaussian Processes (SVGPs) which are well sui…
cs.LG2021
Self-explaining variational posterior distributions for Gaussian Process models
Sarem Seitz
Bayesian methods have become a popular way to incorporate prior knowledge and a notion of uncertainty into machine learning models. At the same time, the complexity of modern machi…
cs.LG2021
Mixtures of Gaussian Processes for regression under multiple prior distributions
Sarem Seitz
When constructing a Bayesian Machine Learning model, we might be faced with multiple different prior distributions and thus are required to properly consider them in a sensible man…