no code implementations • 16 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 $n^{\text{poly}(k/\epsilon)}$.
no code implementations • 12 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 $x^i$.
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 $A - UV$.
