Learning Stable Predictors from Weak Supervision under Distribution Shift
📰 ArXiv cs.AI
Researchers study learning stable predictors from weak supervision under distribution shift, formalizing supervision drift and testing it in CRISPR-Cas13d experiments
Action Steps
- Formalize supervision drift as changes in P(y | x, c) across contexts
- Study supervision drift in CRISPR-Cas13d experiments using RNA-seq responses
- Develop methods to learn stable predictors from weak supervision under distribution shift
- Evaluate the robustness of the learned predictors under different distribution shifts
Who Needs to Know This
Machine learning researchers and data scientists on a team benefit from this study as it provides insights into learning from weak supervision and handling distribution shifts, which is crucial for developing robust models
Key Insight
💡 Supervision drift can significantly impact the robustness of predictors learned from weak supervision, and formalizing it is crucial for developing stable models
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Key Takeaways
Researchers study learning stable predictors from weak supervision under distribution shift, formalizing supervision drift and testing it in CRISPR-Cas13d experiments
Full Article
Title: Learning Stable Predictors from Weak Supervision under Distribution Shift
Abstract:
arXiv:2604.05002v1 Announce Type: cross 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 l
Abstract:
arXiv:2604.05002v1 Announce Type: cross 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 l
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