A Certify-then-Rectify wrapper around HNSW uses conformal risk control plus EVT-estimated graph stretch to deliver average-case HNSW speed with high-probability exact k-NN recovery.
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3 Pith papers cite this work. Polarity classification is still indexing.
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MCI approximates dense nearest neighbor graphs via maximal clique covers and progressive local densification to support fast arbitrary-filtered approximate nearest neighbor search with reduced space.
DLH and DLH-M use distance-aware labeling sets compressed via hashing to support graph range filters in filtered ANN, delivering up to 70.3% higher throughput at over 98.5% recall.
citing papers explorer
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HNSW with Accuracy Guarantees Using Graph Spanners
A Certify-then-Rectify wrapper around HNSW uses conformal risk control plus EVT-estimated graph stretch to deliver average-case HNSW speed with high-probability exact k-NN recovery.
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MCI: A Maximal Clique Index for Efficient Arbitrary-Filtered Approximate Nearest Neighbor Search
MCI approximates dense nearest neighbor graphs via maximal clique covers and progressive local densification to support fast arbitrary-filtered approximate nearest neighbor search with reduced space.
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Approximate Nearest Neighbor Search with Graph Range Filters
DLH and DLH-M use distance-aware labeling sets compressed via hashing to support graph range filters in filtered ANN, delivering up to 70.3% higher throughput at over 98.5% recall.