We consider the problem of learning the nearest neighbor graph of a dataset of n items. The metric is unknown, but we can query an oracle to obtain a noisy estimate of the distance between any pair of items. This framework applies to problem domains where one wants to learn people's preferences from responses commonly modeled as noisy distance judgments. In this paper, we propose an active algorithm to find the graph with high probability and analyze its query complexity. In contrast to existing work that forces Euclidean structure, our method is valid for general metrics, assuming only symmetry and the triangle inequality. Furthermore, we demonstrate efficiency of our method empirically and theoretically, needing only O(n log(n)Δ-2) queries in favorable settings, where Δ-2 accounts for the effect of noise. Using crowd-sourced data collected for a subset of the UT Zappos50K dataset, we apply our algorithm to learn which shoes people believe are most similar and show that it beats both an active baseline and ordinal embedding.

Meeting Name

33rd Conference on Neural Information Processing Systems, NeurIPS 2019 (2019: Dec. 8-14, Vancouver, Canada)


Computer Science


This work was partially supported by AFOSR/AFRL grants FA8750-17-2-0262 and FA9550-18-1-0166.

International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2019 Neural Information Processing Systems Foundation, All rights reserved.

Publication Date

14 Dec 2019