Learning From M -Tuple One-vs-All Confidence Comparison Data

Partial-label learning (PLL) studies classification with ambiguous supervision, where each instance is annotated by a candidate label set that contains the unknown true label. While PLL has been widely investigated, real-world annotation pipelines often provide richer structure than an unordered set: annotators or retrieval systems may implicitly rank candidates, yielding a preferred label that is more likely to be correct, and the mis-selection patterns can be class-dependent rather than unifor