Active Preference Learning for Ordering Items In- and Out-of-sample

arXiv:2405.03059v1 Announce Type: new
Abstract: Learning an ordering of items based on noisy pairwise comparisons is useful when item-specific labels are difficult to assign, for example, when annotators have to make subjective assessments. Algorithms have been proposed for actively sampling comparisons of items to minimize the number of annotations necessary for learning an accurate ordering. However, many ignore shared structure between items, treating them as unrelated, limiting sample efficiency and precluding generalization to new items. In this work, we study …

abstract algorithms annotations arxiv cs.lg example labels sample sampling stat.ml type