Recent advances in Bayesian optimization

X Wang, Y Jin, S Schmitt, M Olhofer - ACM Computing Surveys, 2023 - dl.acm.org
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 …

Perspectives on the integration between first-principles and data-driven modeling

W Bradley, J Kim, Z Kilwein, L Blakely… - Computers & Chemical …, 2022 - Elsevier
Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …

A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization

M Binois, N Wycoff - ACM Transactions on Evolutionary Learning and …, 2022 - dl.acm.org
Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …

Transforming Gaussian processes with normalizing flows

J Maroñas, O Hamelijnck… - International …, 2021 - proceedings.mlr.press
Gaussian Processes (GP) can be used as flexible, non-parametric function priors. Inspired
by the growing body of work on Normalizing Flows, we enlarge this class of priors through a …

Machine-Guided Discovery of Acrylate Photopolymer Compositions

A Jain, CD Armstrong, VR Joseph… - … Applied Materials & …, 2024 - ACS Publications
Additive manufacturing (AM) can be advanced by the diverse characteristics offered by
thermoplastic and thermoset polymers and the further benefits of copolymerization …

Projection pursuit Gaussian process regression

G Chen, R Tuo - IISE Transactions, 2023 - Taylor & Francis
A primary goal of computer experiments is to reconstruct the function given by the computer
code via scattered evaluations. Traditional isotropic Gaussian process models suffer from …

Semiparametric discrete data regression with Monte Carlo inference and prediction

DR Kowal, B Wu - arXiv preprint arXiv:2110.12316, 2021 - arxiv.org
Discrete data are abundant and often arise as counts or rounded data. These data
commonly exhibit complex distributional features such as zero-inflation, over-/under …

Sensitivity prewarping for local surrogate modeling

N Wycoff, M Binois, RB Gramacy - Technometrics, 2022 - Taylor & Francis
In the continual effort to improve product quality and decrease operations costs,
computational modeling is increasingly being deployed to determine feasibility of product …

Predictive Subdata Selection for Computer Models

MC Chang - Journal of Computational and Graphical Statistics, 2023 - Taylor & Francis
An explosion in the availability of rich data from the technological advances is hindering
efforts at statistical analysis due to constraints on time and memory storage, regardless of …

Combining additivity and active subspaces for high-dimensional Gaussian process modeling

M Binois, V Picheny - arXiv preprint arXiv:2402.03809, 2024 - arxiv.org
Gaussian processes are a widely embraced technique for regression and classification due
to their good prediction accuracy, analytical tractability and built-in capabilities for …