3 papers
cs.LG2026
Bayesian Optimization in Linear Time
Jesse Schneider, William J. Welch
Bayesian optimization is a sequential method for minimizing objective functions that are expensive to evaluate and about which few assumptions can be made. By using all gathered da…
math.OC2025
Constrained Efficient Global Optimization of Expensive Black-box Functions
Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic +1
We study the problem of constrained efficient global optimization, where both the objective and constraints are expensive black-box functions that can be learned with Gaussian proc…
stat.ME2025
A Gaussian Sliding Windows Regression Model for Hydrological Inference
Stefan Schrunner, Parham Pishrobat, Joseph Janssen +4
Statistical models are an essential tool to model, forecast and understand the hydrological processes in watersheds. In particular, the understanding of time lags associated with t…