4 papers
Bayesian Optimization with Fisher Information Geometry: Gradient Bounds and Trust-Region Methods
Saksham Kiroriwal, Julius Pfrommer, Jürgen Beyerer
We study Bayesian optimization (BO) through the lens of information geometry. Pulling back the Fisher information metric through the surrogate posterior map yields a local sensitiv…
Virtual Process Dossier: A Process-Aware Data Catalogue
Lukas Kubelka, Alexander Bott, Frank Döhner +6
We propose the Virtual Process Dossier (VPD), a Knowledge Graph-based data catalogue that also captures workflow provenance. We developed VPD for multi-stage manufacturing use-case…
Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimization
Saksham Kiroriwal, Julius Pfrommer, Jürgen Beyerer
Bayesian optimization (BO) is a powerful method for optimizing black-box manufacturing processes, but its performance is often limited when dealing with high-dimensional multi-stag…
Partially Observable Gaussian Process Network and Doubly Stochastic Variational Inference
Saksham Kiroriwal, Julius Pfrommer, Jürgen Beyerer
To reduce the curse of dimensionality for Gaussian processes (GP), they can be decomposed into a Gaussian Process Network (GPN) of coupled subprocesses with lower dimensionality. I…