# Active Ordinal Querying for Tuplewise Similarity Learning

arXiv: https://arxiv.org/abs/1910.04115 · HTML: https://mlsanity.com/paper/1910.04115/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [Direct Judgement Preference Optimization](https://mlsanity.com/paper/2409.14664/) (2024)
- [One for All: Simultaneous Metric and Preference Learning over Multiple Users](https://mlsanity.com/paper/2207.03609/) (2022)
- [Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning](https://mlsanity.com/paper/2309.04626/) (2023)
- [Active metric learning and classification using similarity queries](https://mlsanity.com/paper/2202.01953/) (2022)
- [Active Learning of Ordinal Embeddings: A User Study on Football Data](https://mlsanity.com/paper/2207.12710/) (2022)
- [Beyond Labels: Information-Efficient Human-in-the-Loop Learning using Ranking and Selection Queries](https://mlsanity.com/paper/2602.15738/) (2026)
- [LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordinal Data](https://mlsanity.com/paper/2602.04192/) (2026)

## Abstract (arXiv)

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.