4 papers
Integrated Gradient attribution for Gaussian Processes with non-Gaussian likelihoods
Sarem Seitz
Gaussian Process (GP) models are a powerful tool in probabilistic machine learning with a solid theoretical foundation. Thanks to current advances, modeling complex data with GPs i…
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…
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…
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…