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Found 8 papers, 5 papers with code
Optimizing with Low Budgets: a Comparison on the Black-box Optimization Benchmarking Suite and OpenAI Gym
BO-based algorithms are popular in the ML community, as they are used for hyperparameter optimization and more generally for algorithm configuration.
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets.
Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB
Bayesian Optimization (BO) is a class of black-box, surrogate-based heuristics that can efficiently optimize problems that are expensive to evaluate, and hence admit only small evaluation budgets.
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems.
PI is back! Switching Acquisition Functions in Bayesian Optimization
We also compare this to a random schedule and round-robin selection of EI and PI.
High Dimensional Bayesian Optimization with Kernel Principal Component Analysis
Bayesian Optimization (BO) is a surrogate-based global optimization strategy that relies on a Gaussian Process regression (GPR) model to approximate the objective function and an acquisition function to suggest candidate points.
Benchmarking Algorithms from Machine Learning for Low-Budget Black-Box Optimization
Machine learning has invaded various domains of computer science, including black-box optimization.
High Dimensional Bayesian Optimization Assisted by Principal Component Analysis
Bayesian Optimization (BO) is a surrogate-assisted global optimization technique that has been successfully applied in various fields, e. g., automated machine learning and design optimization.
