In online learning literature, stochastic and adversarial settings are two of the most well-studied cases.

In online learning literature, stochastic and adversarial settings are two of the most well-studied cases.

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

As a result, it tractably draws samples even when the matrices forming the Khatri-Rao product have tens of millions of rows each.

B, which is part of the linear least squares right-hand-side we can solve for.