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- W4387076456 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." @default.
- W4387076456 created "2023-09-27" @default.
- W4387076456 creator A5036036404 @default.
- W4387076456 date "2023-09-25" @default.
- W4387076456 modified "2023-09-28" @default.
- W4387076456 title "Accelerating Machine Learning Algorithms with Adaptive Sampling" @default.
- W4387076456 doi "https://doi.org/10.48550/arxiv.2309.14221" @default.
- W4387076456 hasPublicationYear "2023" @default.
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