למידה עם אי-ודאות ממטרות תחום רב-מומחים

תקציר מקורי באנגליתarXiv:2610.00102v1 Announce Type: new Abstract: Many machine learning (ML) applications rely on expert labels, and qualified experts may provide different but plausible interpretations of the same observation. Such variation across expert labels may reflect genuine disagreement or ambiguity rather than annotation error. When individual experts additionally report intervals rather than exact values, the supervision contains two distinct sources of label uncertainty: within-label imprecision and between-expert variation. Existing methods treat these forms separately: multi-expert approaches collapse labels to a consensus, interval-target methods often yield a single prediction, and predictive-uncertainty methods rarely validate their uncertainty estimates against observed expert disagreement