NFL: Robust Learned Index via Distribution Transformation
Summary: NFL: a two-stage learned index that first applies Numerical Normalizing Flow to transform skewed key distributions into near-uniform, then builds the index on transformed keys. Introduces After-Flow Learned Index (AFLI) for robustness, with experiments showing higher throughput and lower tail latency than state-of-the-art learned indexes on synthetic and real workloads. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Shangyu Wu
- 2. Yufei Cui
- 3. Jinghuan Yu
- 4. Xuan Sun
- 5. Tei-Wei Kuo
- 6. Chun Jason Xue
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