{
  "id": 6369515,
  "title": "Parameterized and Streaming Algorithms for Euclidean Fair $k$-Center Clustering",
  "url": "https://urgent.news/2026/09/06/parameterized-and-streaming-algorithms-for-euclidean-fair-k-center",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-06T04:49:44.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.06384v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Motivated by the growing importance of fairness in machine learning, fair $k$-center clustering has attracted considerable research attention as a fundamental problem. In this problem, a dataset is partitioned into $m$ disjoint groups, and the objective is to select $k$ data points as centers, subject to upper bounds on the number of centers chosen from each group, aiming to minimize the maximum…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}