Some related lower bounds include the work of Backurs et al. [2017] that solving kernel Support V ector Machines (SVM), ridge regression, or Principal Component Analysis (PCA) problems to high accuracy or approximating kernel density estimates up to a constant factor for kernels with

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First, we show the negativeresult that no strongly sublinear sized coresets existforlogisticregression.

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Such complexities are prohibitive for scaling to large datasets.

In the total least squares problem, one is given an m n matrix A, and an m d matrix B, and one seeks to "correct" both A and B, obtaining matrices  and B, so that there exists an X satisfying the equation ÂX = B. Typically the problem is overconstrained, meaning that m max(n, d).

We consider a least squares regression problem where the data has been generated from alinear model, and weareinterested tolearn theunknownregression parameters.

Recently,the Hutch++ algorithm was proposed, which reduces the number of matrix-vector queries fromO(1/2) to the optimalO(1/), and the algorithm succeeds with constant probability.