Paper

Conditional Local Importance by Quantile Expectations

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arXiv:2411. 08821v4 Announce Type: replace Abstract: Global variable importance measures are commonly used to interpret the results of machine learning models.

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Authors: Kelvyn K. Bladen, Adele Cutler, D. Richard Cutler, Kevin R. Moon
Released: 2026-06-17 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Conditional Local Importance by Quantile Expectations overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", 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 "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)". Matching terms: class, latin, rect, eval, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2411.08821

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