Tag
This paper proposes a generalized distribution-free semi-supervised learning framework that constructs unbiased risk estimators via linear combinations of component risks, extending PNU learning to multiclass classification while achieving lower variance and providing generalization bounds.
This paper introduces the Level-Constrained-Littlestone-Littlestone (LCLL) tree to characterize learnability in universal transductive online classification with possibly unbounded label spaces, proving that optimal mistake rates are either bounded or logarithmic.
This paper characterizes approximate property calibration for discrete properties in multiclass classification, using Lipschitz continuous properties as an intermediary to reduce complexity from the number of classes to the elicitation complexity dimension.