Learning Stable Predictors from Weak Supervision under Distribution Shift

• URL: http://arxiv.org/abs/2604.05002v1
• Date: Sun, 05 Apr 2026 23:46:49 GMT
• Title: Learning Stable Predictors from Weak Supervision under Distribution Shift
• Abstract summary: Learning from weak or proxy supervision is common when ground-truth labels are unavailable.<n>We formalize this as supervision drift, defined as changes in P(y | x, c) across contexts.<n>We study it in CRISPR-Cas13d experiments where guide efficacy is inferred indirectly from RNA-seq responses.
• Score: 2.8410059035029955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning from weak or proxy supervision is common when ground-truth labels are unavailable, yet robustness under distribution shift remains poorly understood, especially when the supervision mechanism itself changes. We formalize this as supervision drift, defined as changes in P(y | x, c) across contexts, and study it in CRISPR-Cas13d experiments where guide efficacy is inferred indirectly from RNA-seq responses. Using data from two human cell lines and multiple time points, we build a controlled non-IID benchmark with explicit domain and temporal shifts while keeping the weak-label construction fixed. Models achieve strong in-domain performance (ridge R^2 = 0.356, Spearman rho = 0.442) and partial cross-cell-line transfer (rho ~ 0.40). However, temporal transfer fails across all models, with negative R^2 and near-zero correlation (e.g., XGBoost R^2 = -0.155, rho = 0.056). Additional analyses confirm this pattern. Feature-label relationships remain stable across cell lines but change sharply over time, indicating that failures arise from supervision drift rather than model limitations. These findings highlight feature stability as a simple diagnostic for detecting non-transferability before deployment.

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