activity
20152026
most citedA hierarchical expected improvement method for Bayesian optimization

12 citations · 24 across the 11 of their papers we have counts for

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Showing 2017Show all

5 papers · 1 filter

stat.ME2017

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…

math.ST2017

On Prediction Properties of Kriging: Uniform Error Bounds and Robustness

Wenjia Wang, Rui Tuo, C. F. Jeff Wu

Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simp…

stat.ME2017

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…

stat.ME2017

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…

stat.ME2017★ 1 cited

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…