Search Results for author: Frank Ban

Found 3 papers, 0 papers with code

Regularized Weighted Low Rank Approximation

no code implementations NeurIPS 2019 Frank Ban, David Woodruff, Qiuyi Zhang

The classical low rank approximation problem is to find a rank $k$ matrix $UV$ (where $U$ has $k$ columns and $V$ has $k$ rows) that minimizes the Frobenius norm of $A - UV$.

Efficient average-case population recovery in the presence of insertions and deletions

no code implementations12 Jul 2019 Frank Ban, Xi Chen, Rocco A. Servedio, Sandip Sinha

In this problem, there is an unknown distribution $\cal{D}$ over $s$ unknown source strings $x^1,\dots, x^s \in \{0, 1\}^n$, and each sample is independently generated by drawing some $x^i$ from $\cal{D}$ and returning an independent trace of $x^i$.

A PTAS for $\ell_p$-Low Rank Approximation

no code implementations16 Jul 2018 Frank Ban, Vijay Bhattiprolu, Karl Bringmann, Pavel Kolev, Euiwoong Lee, David P. Woodruff

On the algorithmic side, for $p \in (0, 2)$, we give the first $(1+\epsilon)$-approximation algorithm running in time $n^{\text{poly}(k/\epsilon)}$.

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