25 citations · 70 across the 17 of their papers we have counts for
4 papers · 1 filter
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…
Data-Adaptive Dimensional Analysis for Accurate Interpolation and Extrapolation in Computer Experiments
G. Alexi Rodriguez-Arelis, William J. Welch
Dimensional analysis (DA) pays attention to fundamental physical dimensions such as length and mass when modelling scientific and engineering systems. It goes back at least a centu…
Subsampling Generative Adversarial Networks: Density Ratio Estimation in Feature Space with Softplus Loss
Xin Ding, Z. Jane Wang, William J. Welch
Filtering out unrealistic images from trained generative adversarial networks (GANs) has attracted considerable attention recently. Two density ratio based subsampling methods---Di…
Bayesian Optimization Using Monotonicity Information and Its Application in Machine Learning Hyperparameter
Wenyi Wang, William J. Welch
We propose an algorithm for a family of optimization problems where the objective can be decomposed as a sum of functions with monotonicity properties. The motivating problem is op…