Recent advances in Bayesian optimization
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 …
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
Expected improvement for expensive optimization: a review
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 …
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
Scalable global optimization via local Bayesian optimization
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 …
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
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 …
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 …
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
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 …
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 …
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 …
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 …
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 …
evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful …