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About this paper
Chandrika Kamath
This paper introduces "intelligent sampling," a requirements-driven framework for selecting and adapting sampling algorithms to address complex scientific and machine learning challenges. It demonstrates that farthest-point algorithms, augmented with techniques like Latinization, provide superior space-filling properties, adaptability (e.g., for incremental sampling), and computational efficiency compared to traditional methods like Latin Hypercube Sampling.
Key insights
- Farthest-point algorithms (GreedyFP, Best Candidate) are highly adaptable for progressive/incremental sampling, PDF-based sampling, and non-rectangular regions, addressing key limitations of traditional methods.
- Latin Hypercube Sampling, despite its popularity, is computationally expensive to optimize and lacks inherent support for incremental sampling without complex modifications.
- Relatively simple algorithms, when intelligently chosen and adapted from other fields, can outperform more complex methods in terms of efficiency and effectiveness for specific application needs.
Summary by alphaXiv.
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