F. Ban

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

4 citations · 5 across the 2 of their papers we have counts for

collaborators

4 papers

cs.DS2019★ 1 cited

Regularized Weighted Low Rank Approximation

Frank Ban, David Woodruff, Qiuyi Zhang

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

cs.DS2019★ 4 cited

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

Frank Ban, Xi Chen, Rocco A. Servedio +1

Several recent works have considered the \emph{trace reconstruction problem}, in which an unknown source string $x \in {0, 1}^{n}$ is transmitted through a probabilistic channel which…

cs.DS2019

Beyond trace reconstruction: Population recovery from the deletion channel

Frank Ban, Xi Chen, Adam Freilich +2

\emph{Population recovery} is the problem of learning an unknown distribution over an unknown set of $n$ -bit strings, given access to independent draws from the distribution that h…

cs.DS2018

A PTAS for $ℓ_{p}$ -Low Rank Approximation

Frank Ban, Vijay Bhattiprolu, Karl Bringmann +3

A number of recent works have studied algorithms for entrywise $ℓ_{p}$ -low rank approximation, namely, algorithms which given an $n \times d$ matrix $A$ (with $n \geq d$ ), output…

Regularized Weighted Low Rank Approximation

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

Beyond trace reconstruction: Population recovery from the deletion channel

A PTAS for ℓp​-Low Rank Approximation

A PTAS for $ℓ_{p}$ -Low Rank Approximation