A learning framework for nearest neighbor search

40 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Maximum inner-product search using cone trees (2012, background): “Trees can also be constructed by learning from the data using techniques from machine learning [6, 25] to provide better accuracy and efficiency.”
Randomized Partition Trees for Nearest Neighbor Search (2015, background): “A general framework for adaptivity, with discussion of generalization issues, can be found in [7].”
Learning to Hash for Indexing Big Data—A Survey (2015, methodology): “By converting rank lists to a triplet tensor matrix, listwise hashing is designed to preserve the ranking in the Hamming space [76].”
Learning to Prune in Metric and Non-Metric Spaces (2013, unknown): “There are also a number of approaches, where machine learning is used to estimate optimal parameters of classic search methods [7].”
VIBE: Vector Index Benchmark for Embeddings (2025, unknown): “There is a recent interest for ANN search in the out-of-distribution (OOD) setting (e.g., Cayton and Dasgupta, 2007; Jaiswal et al., 2022; Hyvönen et al., 2022; Chen et al., 2024; Jääsaari et al., 2024; Mazaré et al., 2025), where the distributions of the corpus points and the query points are…”