• Corpus ID: 236987097

Learning to Hash Robustly, with Guarantees

@article{Andoni2021LearningTH,
  title={Learning to Hash Robustly, with Guarantees},
  author={Alexandr Andoni and Daniel Beaglehole},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.05433}
}
The indexing algorithms for the high-dimensional nearest neighbor search (NNS) with the best worst-case guarantees are based on the randomized Locality Sensitive Hashing (LSH), and its derivatives. In practice, many heuristic approaches exist to "learn" the best indexing method in order to speed-up NNS, crucially adapting to the structure of the given dataset. Oftentimes, these heuristics outperform the LSH-based algorithms on real datasets, but, almost always, come at the cost of losing the… 

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References

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TLDR
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