SEED: Targeted Data Selection by Weighted Independent Set
Data selection seeks to identify a compact yet informative subset from large-scale training corpora, balancing sample quality against collection diversity. We formulate this problem as a Weighted Independent Set (WIS) on a similarity graph, where nodes represent data samples weighted by influence, and edges connect semantically redundant pairs. This formulation naturally yields subsets that are simultaneously high-quality and diverse. However, two challenges arise in practice: naive node weights fail to distinguish informative signals from gradient noise, and edge construction under heterogeneous domain distributions produces structurally imbalanced graphs that bias selection toward spars...
| Authors | Yuan Zhang, Lifeng Guo, Junwen Pan et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 May 2026 |
| Last updated at source | 20 May 2026 |
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