arXiv:2205.14090v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions emerge in applications as diverse as hyperparameter tuning, drug discovery, and robotics. BO hinges on a Bayesian surrogate model to sequentially select query points so as to balance exploration with exploitation of the search space. Most existing works rely on a single Gaussian process (GP) based surrogate model, where the kernel function form is typically preselected using domain knowledge. To bypa
This paper represents continued academic progress in the field of Bayesian optimization, addressing known limitations in traditional Gaussian process models.
Improved Bayesian optimization techniques can lead to more efficient and robust black-box function optimization, critical for advancing AI research and applications across various domains.
The ability to move beyond a single Gaussian process model in Bayesian optimization offers enhanced flexibility and potentially better performance in complex optimization tasks.
- · AI researchers
- · Machine learning engineers
- · Drug discovery sector
- · Robotics sector
- · Inefficient optimization methodologies
More efficient optimization of hyperparameters and complex engineering problems.
Faster iteration cycles in AI model development and scientific discovery.
Enhanced AI system performance leading to more advanced applications in diverse industries.
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