AI RESEARCH
Risk-Consistent Multiclass Learning from Random Label-Subset Membership Queries
arXiv CS.LG
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ArXi:2605.07413v1 Announce Type: new Obtaining accurate class labels is often costly or unreliable, and may also be limited by privacy or other practical conditions. Compared with asking an annotator to provide the exact class, it is often easier to ask whether the true label belongs to a certain label subset. This query-response form defines a distinct weak-supervision mechanism: weak supervision information is generated through feedback on a label subset.