Incremental k-center clustering admits no better than 2-approximation even for non-polynomial algorithms, via a new lower-bound construction.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A reference-based geometric hashing method recovers cross-model vector correspondences by exploiting local isometric consistency in contrastive embeddings and iteratively bootstrapping from a seed of paired anchors.
LFM models exhibit stability to data reduction and capacity shrinkage that is tied to the flow matching objective, enabling reduced-data training and coarse-to-fine inference with over 2x speedup.
citing papers explorer
-
The price of incrementality in k-center clustering
Incremental k-center clustering admits no better than 2-approximation even for non-polynomial algorithms, via a new lower-bound construction.