Feb. 9, 2024, 5:42 a.m. | Meysam Alishahi Jeff M. Phillips

cs.LG updates on arXiv.org arxiv.org

We refine and generalize what is known about coresets for classification problems via the sensitivity sampling framework. Such coresets seek the smallest possible subsets of input data, so one can optimize a loss function on the coreset and ensure approximation guarantees with respect to the original data. Our analysis provides the first no dimensional coresets, so the size does not depend on the dimension. Moreover, our results are general, apply for distributional input and can use iid samples, so provide …

analysis approximation classification cs.cg cs.lg data framework function loss refine sampling sensitivity via

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