Improving ANNS through Learned Adaptive Early Termination

VLDB 2023 paper presenting ELPIS, a graph-based similarity search approach that combines graph indexing with learning-based techniques for scalable data science applications on large datasets.

ICML 2019 paper introducing a learned routing approach for similarity graphs, using machine learning to guide greedy search traversal in graph-based approximate nearest neighbor search.

ICDE 2024 paper proposing a routing-guided learned product quantization method that enhances graph-based approximate nearest neighbor search by learning optimal quantization guided by graph routing information.

VLDB 2025 paper proposing efficient level navigation with shortcuts for accelerating approximate nearest neighbor search in hierarchical graph indexes, improving traversal speed across multi-layer graph structures.

SIGKDD 2025 paper proposing adaptive awareness capabilities for graph-based approximate nearest neighbor search, enabling the search algorithm to dynamically adjust its strategy based on local graph characteristics and query properties.

SIGMOD 2025 paper proposing optimizations for graph-based approximate nearest neighbor search indexing on modern CPU architectures, leveraging SIMD instructions and cache-aware algorithms for improved index construction performance.