Experiments are the gold standard for causal inference. In many applicat...
In an era where scientific experimentation is often costly, multi-fideli...
Thompson sampling is a popular algorithm for solving multi-armed bandit
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Kriging (or Gaussian process regression) is a popular machine learning m...
In this paper, we investigate Gaussian process regression with input loc...
Global optimization of expensive functions has important applications in...
Kernel ridge regression is an important nonparametric method for estimat...
Expected improvement (EI) is one of the most popular Bayesian optimizati...
Gaussian processes (GPs) are widely used as surrogate models for emulati...
Calibration refers to the estimation of unknown parameters which are pre...
This interdisciplinary study, which combines machine learning, statistic...
This paper presents a novel method, called Analysis-of-marginal-Tail-Mea...
Kriging based on Gaussian random fields is widely used in reconstructing...
The present study proposes a data-driven framework trained with high-fid...