Many machine learning tasks such as clustering, classification, and dataset
search benefit from embedding data points in a space where distances reflect
notions of relative similarity as perceived by humans. A common way to
construct such an embedding is to request triplet similarity queries to an
oracle, comparing two objects with respect to a reference. This work
generalizes triplet queries to tuple queries of arbitrary size that ask an
oracle to rank multiple objects against a reference, and introduces an
efficient and robust adaptive selection method called InfoTuple that uses a
novel approach to mutual information maximization. We show that the performance
of InfoTuple at various tuple sizes exceeds that of the state-of-the-art
adaptive triplet selection method on synthetic tests and new human response
datasets, and empirically demonstrate the significant gains in efficiency and
query consistency achieved by querying larger tuples instead of triplets.

该论文介绍了一个通用的多元组查询方法，称为 InfoTuple，通过相互信息最大化采用一种新颖的方法，以实现多元组查询的自适应选择。在合成测试和新的人类响应数据集上，该方法在各种元组大小的性能均优于最先进的自适应三元组选择方法，并在经验上证明了通过查询更大的元组而不是三元组实现的查询效率和一致性的重大收益。