学习哈希稳健，有保证
Learning to Hash Robustly, Guaranteed
摘要：
具有最佳最坏情况保证的高维最近邻搜索（NNS）的索引算法是基于随机化的局部敏感哈希（LSH）及其衍生物。在实践中，存在许多启发式方法来“学习”最佳索引方法，以加速NNS，关键是适应给定数据集的结构。通常情况下，这些算法在真实数据集上的性能优于基于LSH的算法，但几乎总是以失去对抗性查询的正确性或鲁棒性能的保证为代价，或者适用于具有假设的额外结构/模型的数据集。在本文中，我们为汉明空间设计了一个NNS算法，该算法具有最坏情况下的保证，基本上与理论算法相匹配，同时优化了数据集结构的散列（考虑实例最优算法），以实现最低性能查询。我们评估算法的能力，优化一个给定的数据集在理论上和实践中。在理论方面，我们展示了一个自然的设置（数据集模型），我们的算法比标准的理论好得多。在实践方面，我们运行的实验表明，我们的算法在对MNIST和ImageNet数据集的最差查询上具有1.8倍和2.1倍的召回率。
英文摘要：
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 guarantees of either correctness or robust performance on adversarial queries, or apply to datasets with an assumed extra structure/model. In this paper, we design an NNS algorithm for the Hamming space that has worst-case guarantees essentially matching that of theoretical algorithms, while optimizing the hashing to the structure of the dataset (think instance-optimal algorithms) for performance on the minimum-performing query. We evaluate the algorithm's ability to optimize for a given dataset both theoretically and practically. On the theoretical side, we exhibit a natural setting (dataset model) where our algorithm is much better than the standard theoretical one. On the practical side, we run experiments that show that our algorithm has a 1.8x and 2.1x better recall on the worst-performing queries to the MNIST and ImageNet datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
