1 code implementation • 4 Mar 2024 • Lars Gottesbüren, Laxman Dhulipala, Rajesh Jayaram, Jakub Lacki
In particular, our new routing methods enable the use of balanced graph partitioning, which is a high-quality partitioning method without a naturally associated routing algorithm.
1 code implementation • 1 Feb 2024 • Joshua Engels, Benjamin Landrum, Shangdi Yu, Laxman Dhulipala, Julian Shun
We define and investigate the problem of $\textit{c-approximate window search}$: approximate nearest neighbor search where each point in the dataset has a numeric label, and the goal is to find nearest neighbors to queries within arbitrary label ranges.
no code implementations • 7 Aug 2023 • Laxman Dhulipala, Jason Lee, Jakub Łącki, Vahab Mirrokni
Our algorithm is based on a new approach to computing $(1+\epsilon)$-approximate HAC, which is a novel combination of the nearest-neighbor chain algorithm and the notion of $(1+\epsilon)$-approximate HAC.
1 code implementation • 7 May 2023 • Magdalen Dobson Manohar, Zheqi Shen, Guy E. Blelloch, Laxman Dhulipala, Yan Gu, Harsha Vardhan Simhadri, Yihan Sun
Our algorithms are deterministic and achieve high scalability across a diverse set of challenging datasets.
1 code implementation • 27 Jul 2021 • Jessica Shi, Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirrokni
Our empirical evaluation shows that this framework improves the state-of-the-art trade-offs between speed and quality of scalable community detection.
no code implementations • 10 Jun 2021 • Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirrokni, Jessica Shi
For this variant, this is the first exact algorithm that runs in subquadratic time, as long as $m=n^{2-\epsilon}$ for some constant $\epsilon > 0$.
2 code implementations • 8 Jun 2021 • Shangdi Yu, Yiqiu Wang, Yan Gu, Laxman Dhulipala, Julian Shun
This paper studies the hierarchical clustering problem, where the goal is to produce a dendrogram that represents clusters at varying scales of a data set.