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Found 8 papers, 6 papers with code
Vanilla Bayesian Optimization Performs Great in High Dimensions
High-dimensional problems have long been considered the Achilles' heel of Bayesian optimization algorithms.
A General Framework for User-Guided Bayesian Optimization
The optimization of expensive-to-evaluate black-box functions is prevalent in various scientific disciplines.
High-dimensional Bayesian Optimization with Group Testing
We propose a group testing approach to identify active variables to facilitate efficient optimization in these domains.
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance.
Self-Correcting Bayesian Optimization through Bayesian Active Learning
Gaussian processes are the model of choice in Bayesian optimization and active learning.
Joint Entropy Search for Maximally-Informed Bayesian Optimization
As a light-weight approach with superior results, JES provides a new go-to acquisition function for Bayesian optimization.
$π$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
To address this issue, we propose $\pi$BO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user.
$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
To address this issue, we propose $\pi$BO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user.
