arXiv:2502.06178v5 Announce Type: replace-cross Abstract: Bayesian optimization is highly effective for optimizing expensive-to-evaluate black-box functions, but it faces significant computational challenges due to the cubic per-iteration cost of Gaussian processes, which results in a total time complexity that is quartic with respect to the number of iterations. To address this limitation, we propose a novel algorithm, Bayesian optimization by kernel regression and density-based exploration (BOKE). BOKE uses kernel regression for efficient function approximation, kernel density for exploratio
The continuous drive for more efficient and scalable AI optimization techniques is leading to innovations like BOKE, addressing computational bottlenecks in complex models.
This breakthrough significantly improves the efficiency and scalability of Bayesian optimization, crucial for accelerating AI research and deployment in resource-intensive applications.
Optimizing expensive-to-evaluate black-box functions becomes dramatically faster and more practical (from cubic to linear time complexity per iteration), enabling better model training and discovery.
- · AI/ML researchers
- · Cloud AI providers
- · Biotech and drug discovery
- · Autonomous systems developers
- · Organizations reliant on inefficient black-box optimization
AI model development cycles will shorten and become more cost-effective.
More complex and data-intensive AI applications become feasible, leading to new categories of AI products.
Accelerated scientific discovery in fields heavily reliant on black-box optimization, potentially leading to breakthroughs in materials science or medicine.
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Read at arXiv cs.LG