Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimension
arXiv:2605.23473v1 Announce Type: new Abstract: Bayesian optimization is widely employed for optimizing complex black-box functions but struggles with the curse of dimensionality. Random embedding, as a dimension reduction strategy, simplifies tasks that possess the effective dimension by optimizing within a low-dimensional subspace. However, determining the effective dimension of a task in advance remains a significant challenge, which influences the selection of the subspace dimensionality and the optimization performance. Traditional methods use fixed subspace dimensions provided by experts
The proliferation of complex black-box functions in AI and machine learning necessitates more efficient optimization techniques to overcome the curse of dimensionality.
This development enhances the practical application of Bayesian optimization, making it more effective for high-dimensional problems without requiring expert-defined parameters.
Optimizing complex systems will become more efficient and accessible, as the need for prior knowledge about the effective dimension is removed.
- · AI/ML researchers
- · Industries relying on black-box optimization
- · Automated machine learning platforms
- · Expert-driven manual optimization methods
More robust and generalizable AI models will be developed faster due to improved optimization.
This could accelerate scientific discovery and engineering design processes across various fields.
The reduced computational overhead and expertise requirements might democratize access to advanced optimization techniques, fostering broader innovation.
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Read at arXiv cs.LG