mlsanityData to 14 Oct 2026

The Core in Max-Loss Non-Centroid Clustering Can Be Empty

arXiv 2511.19107 ↗First posted 24 Nov 2025cs.LG cs.AI cs.GTcited by 1

Not on the map. Why?

Abstract (arXiv)

We study core stability in non-centroid clustering under the max-loss objective, where each agent's loss is the maximum distance to other members of their cluster. We prove that for all k≥ 3 there exist metric instances with n≥ 9 agents, with n divisible by k, for which no clustering lies in the α-core for any α<2^frac15~ 1.148. The bound is tight for our construction. Using a computer-aided proof, we also identify a two-dimensional Euclidean point set whose associated lower bound is slightly smaller than that of our general construction. This is, to our knowledge, the first impossibility result showing that the core can be empty in non-centroid clustering under the max-loss objective.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 9 references that are in the map. First posted on arXiv 24 Nov 2025.

Builds on

4 of 9 references.

References · 4

  1. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  2. 2019Proportionally Fair Clusteringcited by 69
  3. 2023Proportional Fairness in Clustering: A Social Choice Perspectivecited by 13
  4. 2024Proportional Fairness in Non-Centroid Clusteringcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.