New FruBO framework prioritizes computational efficiency in Bayesian Optimization

Researchers have introduced FruBO, a new framework for Bayesian Optimization that prioritizes computational efficiency alongside performance. Their study, which benchmarked Gaussian Processes, Random Forests, NGBoost, and Bayesian Adaptive Spline Surfaces across various scientific and machine learning tasks, found that Gaussian Processes were computationally expensive without offering superior results. FruBO aims to guide users in selecting the most suitable surrogate models based on dataset characteristics, offering a reproducible and compute-aware baseline for optimization under limited resources. AI

IMPACT Provides practical guidance for selecting computationally efficient surrogate models in data-limited AI research and applications.

RANK_REASON The cluster contains an academic paper detailing a new methodology and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Bayesian Adaptive Spline Surfaces
• Bayesian Optimization
• Gaussian Processes
• NGBoost
• Panagiotis Krokidas
• Random Forests

• paper
• other

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