4 citations · 6 across the 6 of their papers we have counts for
7 papers
Pivot based correlation clustering in the presence of good clusters
David Rasmussen Lolck, Mikkel Thorup, Shuyi Yan
The classic pivot based clustering algorithm of Ailon, Charikar and Chawla [JACM'08] is factor 3, but all concrete examples showing that it is no better than 3 are based on some ve…
Static to Dynamic Correlation Clustering
Nairen Cao, Vincent Cohen-Addad, Euiwoong Lee +7
Correlation clustering is a well-studied problem, first proposed by Bansal, Blum, and Chawla [Mach. Learn. '04]. The input is an unweighted, undirected graph. The problem is to clu…
Solving the Correlation Cluster LP in Sublinear Time
Nairen Cao, Vincent Cohen-Addad, Shi Li +7
Correlation Clustering is a fundamental and widely-studied problem in unsupervised learning and data mining. The input is a graph and the goal is to construct a clustering minimizi…
Quantum Advantage with Faulty Oracle
David Rasmussen Lolck, Laura Mančinska, Manaswi Paraashar
This paper investigates the impact of noise in the quantum query model, a fundamental framework for quantum algorithms. We focus on the scenario where the oracle is subject to non-…
Combinatorial Correlation Clustering
Vincent Cohen-Addad, David Rasmussen Lolck, Marcin Pilipczuk +3
Correlation Clustering is a classic clustering objective arising in numerous machine learning and data mining applications. Given a graph , the goal is to partition the ve…
A mathematical foundation for self-testing: Lifting common assumptions
Pedro Baptista, Ranyiliu Chen, Jędrzej Kaniewski +4
In this work we study the phenomenon of self-testing from the first principles, aiming to place this versatile concept on a rigorous mathematical footing. Self-testing allows a cla…
Shannon meets Gray: Noise-robust, Low-sensitivity Codes with Applications in Differential Privacy
David Rasmussen Lolck, Rasmus Pagh
Integer data is typically made differentially private by adding noise from a Discrete Laplace (or Discrete Gaussian) distribution. We study the setting where differential privacy o…