3 papers
cs.LG2026
GpFlash: Maximizing Speed and Minimizing Memory for Gaussian Process Training and Prediction
Andre Marchildon
This paper presents GpFlash, a Gaussian process library that is focused on fast training and posterior evaluations, minimal memory usage, and scalability to high-dimensional proble…
math.OC2025
A Framework for Nonlinearly-Constrained Gradient-Enhanced Local Bayesian Optimization with Comparisons to Quasi-Newton Optimizers
André L. Marchildon, David W. Zingg
Bayesian optimization is a popular and versatile approach that is well suited to solve challenging optimization problems. Their popularity comes from their effective minimization o…
math.OC2025
Efficient Gradient-Enhanced Bayesian Optimizer with Comparisons to Conjugate-Gradient and Quasi-Newton Optimizers for Unconstrained Local Optimization
André L. Marchildon, David W. Zingg
The probabilistic surrogates used by Bayesian optimizers make them popular methods when function evaluations are noisy or expensive to evaluate. While Bayesian optimizers are tradi…