Researchers have introduced BO-pro-c, a novel Bayesian optimization algorithm that utilizes a product-of-experts Gaussian process (GP) model. This approach addresses the computational limitations of traditional GP models, which struggle with large datasets due to their cubic complexity. By combining multiple local GP experts, GP-pro-c offers improved uncertainty quantification and reduced computational costs while preserving global correlations. Experiments show that BO-pro-c achieves competitive optimization performance, with a 0.9% reduction in simple regret and a 39.4% decrease in computational overhead compared to standard GP-based Bayesian optimization. AI
IMPACT This new algorithm could enable more efficient large-scale optimization tasks in machine learning.
RANK_REASON The cluster contains a research paper detailing a new algorithm and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Bayesian optimization
- BO-pro-c
- DagsHub
- Gaussian process
- GP-pro-c
- Hugging Face
- IArxiv
- Product-of-Experts Gaussian Process
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