New derivative Gaussian Process method cuts computational cost

Researchers have developed a new derivative Gaussian Process (GP) method that significantly reduces the computational cost of incorporating gradient observations. This approach uses a budget of only two directions per gradient, focusing on direct and indirect contributions to target prediction. When integrated with a Vecchia approximation, this method achieves accuracy comparable to existing exact gradient-reduction techniques while requiring substantially less time and memory, even outperforming function-only GP baselines. AI

IMPACT This research could lead to more efficient and accurate surrogate models in machine learning, potentially impacting areas requiring complex simulations or optimizations.

RANK_REASON Academic paper detailing a new method for Gaussian Processes. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Gaussian process
• Hugging Face
• Vecchia approximation

• paper
• other

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