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no code implementations • 19 Jan 2021 • Karl Bringmann, André Nusser

Computing the similarity of two point sets is a ubiquitous task in medical imaging, geometric shape comparison, trajectory analysis, and many more settings.

Computational Geometry Computational Complexity

no code implementations • NeurIPS 2020 • Amir Abboud, Arturs Backurs, Karl Bringmann, Marvin Künnemann

In this paper we consider lossless compression schemes, and ask if we can run our computations on the compressed data as efficiently as if the original data was that small.

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 • 2 Mar 2018 • Karl Bringmann, Sergio Cabello, Michael T. M. Emmerich

It is known that the problem can be solved in polynomial time in the plane, while the best known running time in any dimension $d \ge 3$ is $\Omega\big(\binom{n}{k}\big)$.

Computational Geometry Data Structures and Algorithms F.2.2

no code implementations • NeurIPS 2017 • Karl Bringmann, Pavel Kolev, David Woodruff

For small $\psi$, our approximation factor is $1+o(1)$.

no code implementations • 30 Oct 2017 • Karl Bringmann, Pavel Kolev, David P. Woodruff

For small $\psi$, our approximation factor is $1+o(1)$.

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