@techreport{cs2-20-1,
  title = {{Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs}},
  author = {Malkov, Yu. A. and Yashunin, D. A.},
  year = {2018},
  url = {https://arxiv.org/abs/1603.09320v4},
  institution = {arXiv:1603.09320v4; first submitted 2016}
}

@techreport{cs2-20-2,
  title = {{Billion-scale similarity search with GPUs}},
  author = {Johnson, Jeff and Douze, Matthijs and Jégou, Hervé},
  year = {2017},
  url = {https://arxiv.org/abs/1702.08734v1},
  doi = {10.48550/arXiv.1702.08734},
  institution = {arXiv:1702.08734v1}
}

@inproceedings{cs2-20-3,
  title = {{DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node}},
  author = {Subramanya, Suhas Jayaram and Devvrit,  and Kadekodi, Rohan and Krishaswamy, Ravishankar and Simhadri, Harsha Vardhan},
  year = {2019},
  url = {https://proceedings.neurips.cc/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf},
  booktitle = {Advances in Neural Information Processing Systems 32}
}

@inproceedings{cs2-20-4,
  title = {{Accelerating Large-Scale Inference with Anisotropic Vector Quantization}},
  author = {Guo, Ruiqi and Sun, Philip and Lindgren, Erik and Geng, Quan and Simcha, David and Chern, Felix and Kumar, Sanjiv},
  year = {2020},
  url = {https://proceedings.mlr.press/v119/guo20h.html},
  booktitle = {PMLR 119, ICML 2020}
}

@techreport{cs2-20-5,
  title = {{ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data}},
  author = {Patel, Liana and Kraft, Peter and Guestrin, Carlos and Zaharia, Matei},
  year = {2024},
  url = {https://arxiv.org/abs/2403.04871v1},
  doi = {10.48550/arXiv.2403.04871},
  institution = {arXiv:2403.04871v1}
}

@techreport{cs2-20-6,
  title = {{Worst-case Performance of Popular Approximate Nearest Neighbor Search Implementations: Guarantees and Limitations}},
  author = {Indyk, Piotr and Xu, Haike},
  year = {2023},
  url = {https://arxiv.org/abs/2310.19126v1},
  doi = {10.48550/arXiv.2310.19126},
  institution = {arXiv:2310.19126v1; accepted at NeurIPS 2023}
}