12 citations · 24 across the 11 of their papers we have counts for
9 papers · 1 filter
Sampling-Based Batch Sequential Design by Stein Variational Gradient Descent
Penghui Fu, Xiaoxian Ding, Chunlin Ji +2
Many real-world experimental design problems require a batch of experimental runs across stages, in which multiple points are selected and evaluated at each stage. However, most wo…
Conglomerate Multi-Fidelity Gaussian Process Modeling, with Application to Heavy-Ion Collisions
Yi Ji, Henry Shaowu Yuchi, Derek Soeder +5
In an era where scientific experimentation is often costly, multi-fidelity emulation provides a powerful tool for predictive scientific computing. While there has been notable work…
A hierarchical expected improvement method for Bayesian optimization
Zhehui Chen, Simon Mak, C. F. Jeff Wu
The Expected Improvement (EI) method, proposed by Jones et al. (1998), is a widely-used Bayesian optimization method, which makes use of a fitted Gaussian process model for efficie…
Analysis-of-marginal-Tail-Means (ATM): a robust method for discrete black-box optimization
Simon Mak, C. F. Jeff Wu
We present a new method, called Analysis-of-marginal-Tail-Means (ATM), for effective robust optimization of discrete black-box problems. ATM has important applications to many real…
A generalized Gaussian process model for computer experiments with binary time series
Chih-Li Sung, Ying Hung, William Rittase +2
Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a genera…
A fresh look at effect aliasing and interactions: some new wine in old bottles
C. F. Jeff Wu
Interactions and effect aliasing are among the fundamental concepts in experimental design. In this paper, some new insights and approaches are provided on these subjects. In the l…
cmenet: a new method for bi-level variable selection of conditional main effects
Simon Mak, C. F. Jeff Wu
This paper introduces a novel method for selecting main effects and a set of reparametrized effects called conditional main effects (CMEs), which capture the conditional effect of…
A theoretical framework for calibration in computer models: parametrization, estimation and convergence properties
Rui Tuo, C. F. Jeff Wu
Calibration parameters in deterministic computer experiments are those attributes that cannot be measured or available in physical experiments. Kennedy and O'Hagan \cite{kennedy200…
Efficient Calibration for Imperfect Computer Models
Rui Tuo, C. F. Jeff Wu
Many computer models contain unknown parameters which need to be estimated using physical observations. Kennedy and O'Hagan (2001) shows that the calibration method based on Gaussi…