Proves that conditional residual answer entropy sets the query-time scale under a routed atom-budgeted certified-repair learned-index architecture.
Shane Culpepper, and Renata Borovica-Gajic
4 Pith papers cite this work, alongside 20 external citations. Polarity classification is still indexing.
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A dual-objective learned index for LSM trees co-optimizes block partitioning and lookup error, with an RL agent tuning parameters, and reports 1.19-2.21x throughput gains in RocksDB.
A unified benchmark shows learned indexes beat fence pointers on memory-latency tradeoff in LSM-trees, with position boundary and SSTable granularity as the key tuning knobs.
An experimental evaluation of learned spatial indexes derives a decision tree for index selection under varying data skew, query selectivity, and storage conditions, validated on real point sets.
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Residual-Entropy Accounting for Routed Atom-Budgeted Learned Indexes
Proves that conditional residual answer entropy sets the query-time scale under a routed atom-budgeted certified-repair learned-index architecture.