Accelerating Machine Learning Algorithms with Adaptive Sampling

• URL: http://arxiv.org/abs/2309.14221v1
• Date: Mon, 25 Sep 2023 15:25:59 GMT
• Title: Accelerating Machine Learning Algorithms with Adaptive Sampling
• Authors: Mo Tiwari
• Abstract summary: It is often sufficient to substitute computationally intensive subroutines with a special kind of randomized counterparts that results in almost no degradation in quality. This thesis demonstrates that it is often sufficient, instead, to substitute computationally intensive subroutines with a special kind of randomized counterparts that results in almost no degradation in quality.
• Score: 1.539582851341637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The era of huge data necessitates highly efficient machine learning algorithms. Many common machine learning algorithms, however, rely on computationally intensive subroutines that are prohibitively expensive on large datasets. Oftentimes, existing techniques subsample the data or use other methods to improve computational efficiency, at the expense of incurring some approximation error. This thesis demonstrates that it is often sufficient, instead, to substitute computationally intensive subroutines with a special kind of randomized counterparts that results in almost no degradation in quality.

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