Optimal Sampling for Statistical Modeling and Validation
Author(s)
Vakayil, Akhil
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Abstract
In statistics and machine learning, often we need to sample from or partition data, e.g., for generating training-testing splits, subsampling for tractable statistical analysis, etc. This thesis presents an optimal sampling/partitioning methodology and its applications. Chapter 1 provides the motivation behind the proposed methodology from a validation perspective, Chapter 2 gives an efficient algorithm that makes the optimal sampling applicable to large datasets, and finally, Chapter 3 presents a novel Gaussian process approximation exploiting the proposed sampling methodology.
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Date
2023-05-18
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Text
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Dissertation