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News/Learning Stable Predictors from Weak Supervision under Distribution Shift
arxiv
PublishedMay 21, 2026 at 4:00 AM
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Learning Stable Predictors from Weak Supervision under Distribution Shift

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arXiv:2604.05002v3 Announce Type: replace-cross Abstract: Learning from weak, proxy, or relative supervision is common when ground-truth labels are unavailable, but robustness under distribution shift remains poorly understood because the supervision mechanism itself may change across environments.

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