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 …
BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
Bayesian optimization (BO) has become an established framework and popular tool for
hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its …
hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its …
Bayesian optimization with a prior for the optimum
Abstract While Bayesian Optimization (BO) is a very popular method for optimizing
expensive black-box functions, it fails to leverage the experience of domain experts. This …
expensive black-box functions, it fails to leverage the experience of domain experts. This …
TREGO: a trust-region framework for efficient global optimization
Efficient global optimization (EGO) is the canonical form of Bayesian optimization that has
been successfully applied to solve global optimization of expensive-to-evaluate black-box …
been successfully applied to solve global optimization of expensive-to-evaluate black-box …
Output-weighted optimal sampling for Bayesian experimental design and uncertainty quantification
We introduce a class of acquisition functions for sample selection that lead to faster
convergence in applications related to Bayesian experimental design and uncertainty …
convergence in applications related to Bayesian experimental design and uncertainty …
Embedding high-dimensional bayesian optimization via generative modeling: parameter personalization of cardiac electrophysiological models
The estimation of patient-specific tissue properties in the form of model parameters is
important for personalized physiological models. Because tissue properties are spatially …
important for personalized physiological models. Because tissue properties are spatially …
Adaptive nonparametric psychophysics
L Owen, J Browder, B Letham, G Stocek… - arxiv preprint arxiv …, 2021 - arxiv.org
We introduce a new set of models and adaptive psychometric testing methods for
multidimensional psychophysics. In contrast to traditional adaptive staircase methods like …
multidimensional psychophysics. In contrast to traditional adaptive staircase methods like …
Cylindrical Thompson Sampling for High-Dimensional Bayesian Optimization
B Rashidi, K Johnstonbaugh… - … Conference on Artificial …, 2024 - proceedings.mlr.press
Many industrial and scientific applications require optimization of one or more objectives by
tuning dozens or hundreds of input parameters. While Bayesian optimization has been a …
tuning dozens or hundreds of input parameters. While Bayesian optimization has been a …
Cautious bayesian optimization for efficient and scalable policy search
Sample efficiency is one of the key factors when applying policy search to real-world
problems. In recent years, Bayesian Optimization (BO) has become prominent in the field of …
problems. In recent years, Bayesian Optimization (BO) has become prominent in the field of …
Constrained Bayesian optimization with a cardiovascular application
L Mihaela Paun… - … of the Royal …, 2024 - royalsocietypublishing.org
The present paper investigates constrained global optimization techniques for
computationally expensive black box functions that are globally defined but subject to some …
computationally expensive black box functions that are globally defined but subject to some …