← Back to all news
arXiv Machine Learning By Sami Davies, Benjamin Moseley, Heather Newman

Online Correlation Clustering: Simultaneously Optimizing All $\ell_p$-norms

Read the original on arXiv Machine Learning →

arXiv:2510. 15076v2 Announce Type: replace Abstract: The $\ell_p$-norm objectives for correlation clustering present a fundamental trade-off between minimizing total disagreements (the $\ell_1$-norm) and ensuring fairness to individual nodes (the $\ell_\infty$-norm).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Aug 12

High-Dimensional Calibration from Swap Regret

arXiv:2505. 21460v2 Announce Type: replace Abstract: We study online calibration of multi-dimensional forecasts over an arbitrary convex set $P \subset \mathbb{R}^d$ relative to an arbitrary norm $|\cdot|$.

By Maxwell Fishelson, Noah Golowich, Mehryar Mohri, Jon Schneider
More like this →
arXiv Machine Learning
Jun 30

Clustering with Non-adaptive Subset Queries

arXiv:2409. 10908v3 Announce Type: replace-cross Abstract: Recovering the underlying $k$-clustering of a set $U$ of $n$ points by asking pair-wise same-cluster queries has garnered significant interest in the past few years.

By Hadley Black, Euiwoong Lee, Arya Mazumdar, Barna Saha
More like this →
arXiv Machine Learning
Jul 3

Incremental (k, z)-Clustering on Graphs

arXiv:2602. 08542v3 Announce Type: replace-cross Abstract: Given a weighted undirected graph, a number of clusters $k$, and an exponent $z$, the goal in the $(k, z)$-clustering problem on graphs is to select $k$ vertices as centers that minimize the sum of the distances raised to the power $z$ of each vertex to its closest center.

By Emilio Cruciani, Sebastian Forster, Antonis Skarlatos
safety
More like this →