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Found 3 papers, 0 papers with code
Near-Linear Time and Fixed-Parameter Tractable Algorithms for Tensor Decompositions
Our key technique is a method for obtaining subspace embeddings with a number of rows polynomial in $q$ for a matrix which is the flattening of a tensor train of $q$ tensors.
An Efficient Protocol for Distributed Column Subset Selection in the Entrywise $\ell_p$ Norm
We give a distributed protocol with nearly-optimal communication and number of rounds for Column Subset Selection with respect to the entrywise {$\ell_1$} norm ($k$-CSS$_1$), and more generally, for the $\ell_p$-norm with $1 \leq p < 2$.
Optimal $\ell_1$ Column Subset Selection and a Fast PTAS for Low Rank Approximation
We give the first polynomial time column subset selection-based $\ell_1$ low rank approximation algorithm sampling $\tilde{O}(k)$ columns and achieving an $\tilde{O}(k^{1/2})$-approximation for any $k$, improving upon the previous best $\tilde{O}(k)$-approximation and matching a prior lower bound for column subset selection-based $\ell_1$-low rank approximation which holds for any $\text{poly}(k)$ number of columns.
