AI RESEARCH
Learning Stable Predictors from Weak Supervision under Distribution Shift
arXiv CS.AI
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ArXi:2604.05002v1 Announce Type: cross 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.