Best-Arm Identification-Based Trust Region Selection for Bayesian Optimization on Multimodal Functions
arXiv:2605.31050v1 Announce Type: new Abstract: Gaussian process-based Bayesian optimization (BO) is a popular approach for expensive black-box optimization, but its performance often degrades on complex multimodal or high-dimensional problems. Trust region-based BO mitigates this issue by focusing on local regions, and recent studies suggest that selecting an effective region can be formulated as a multi-armed bandit problem. We propose a trajectory-aware framework that integrates best-arm identification (BAI) with trust region-based BO to efficiently solve multimodal optimization problems. O
The continuous evolution of AI research seeks more efficient and robust optimization techniques for complex problems as foundational models become more sophisticated.
Improved Bayesian Optimization methods directly impact the efficiency and effectiveness of developing, training, and fine-tuning advanced AI models, particularly in complex or resource-constrained environments.
This research offers a more efficient approach to multimodal optimization for expensive black-box functions, potentially accelerating the development cycle for complex AI systems and scientific discovery.
- · AI research and development (R&D)
- · Machine learning engineers
- · Companies with high-dimensional optimization problems
- · Drug discovery and materials science
- · Inefficient black-box optimization methods
- · Organizations relying solely on brute-force search
More accurate and faster training of large, complex AI models by optimizing their vast parameter spaces.
Accelerated scientific discovery and engineering design processes through more efficient search in complex experimental landscapes.
This could contribute to the development of more capable AI agents if optimization speed and efficiency are key bottlenecks.
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Read at arXiv cs.LG