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 …

Expected improvement for expensive optimization: a review

D Zhan, H Xing - Journal of Global Optimization, 2020 - Springer
The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …

Scalable global optimization via local Bayesian optimization

D Eriksson, M Pearce, J Gardner… - Advances in neural …, 2019 - proceedings.neurips.cc
Bayesian optimization has recently emerged as a popular method for the sample-efficient
optimization of expensive black-box functions. However, the application to high-dimensional …

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 …

High-dimensional Bayesian optimization with sparse axis-aligned subspaces

D Eriksson, M Jankowiak - Uncertainty in Artificial …, 2021 - proceedings.mlr.press
Bayesian optimization (BO) is a powerful paradigm for efficient optimization of black-box
objective functions. High-dimensional BO presents a particular challenge, in part because …

Learning search space partition for black-box optimization using monte carlo tree search

L Wang, R Fonseca, Y Tian - Advances in Neural …, 2020 - proceedings.neurips.cc
High dimensional black-box optimization has broad applications but remains a challenging
problem to solve. Given a set of samples xi, yi, building a global model (like Bayesian …

A framework for Bayesian optimization in embedded subspaces

A Nayebi, A Munteanu… - … Conference on Machine …, 2019 - proceedings.mlr.press
We present a theoretically founded approach for high-dimensional Bayesian optimization
based on low-dimensional subspace embeddings. We prove that the error in the Gaussian …

Scalable constrained Bayesian optimization

D Eriksson, M Poloczek - International Conference on …, 2021 - proceedings.mlr.press
The global optimization of a high-dimensional black-box function under black-box
constraints is a pervasive task in machine learning, control, and engineering. These …

Re-examining linear embeddings for high-dimensional Bayesian optimization

B Letham, R Calandra, A Rai… - Advances in neural …, 2020 - proceedings.neurips.cc
Bayesian optimization (BO) is a popular approach to optimize expensive-to-evaluate black-
box functions. A significant challenge in BO is to scale to high-dimensional parameter …

Increasing the scope as you learn: Adaptive Bayesian optimization in nested subspaces

L Papenmeier, L Nardi… - Advances in Neural …, 2022 - proceedings.neurips.cc
Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-
evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful …