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Cluster LOCO: Feature Importance For Interpreting Clusters

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arXiv:2606. 14592v1 Announce Type: new Abstract: Clustering is widely used for exploratory analysis and scientific discovery, driving insights from market segmentation to biological data analysis, but its outputs can be difficult to interpret, audit, and reproduce as modern datasets become increasingly large and complex.

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Authors: Claire M. He, Genevera I. Allen
Released: 2026-06-15 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Cluster LOCO: Feature Importance For Interpreting Clusters overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe". Matching terms: under, rate, compare, lora, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.14592

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