Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

Modeling data uncertainty is important for noisy images, but seldom explored for face
recognition. The pioneer work, PFE, considers uncertainty by modeling each face image …

Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …

We present a tutorial on Bayesian optimization, a method of finding the maximum of
expensive cost functions. Bayesian optimization employs the Bayesian technique of setting …

We give a basic introduction to Gaussian Process regression models. We focus on
understanding the role of the stochastic process and how it is used to define a distribution …

We describe the maximum-likelihood parameter estimation problem and how the
Expectation-Maximization (EM) algorithm can be used for its solution. We first describe the …

We introduce Bayesian optimization, a technique developed for optimizing time-consuming
engineering simulations and for fitting machine learning models on large datasets. Bayesian …

Gaussian processes (GPs) have experienced tremendous success in biogeophysical
parameter retrieval in the last few years. GPs constitute a solid Bayesian framework to …