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Learning Linear Regression Models over Factorized Joins

Summary: Learning linear regression on training data defined by arbitrary joins using factorized representations. Proposes F/FDB, F, F/SQL to factorize cofactors, decouple gradient updates from convergence, and exploit join/union commutativity; factorized joins can be exponentially cheaper, delivering up to 1000x speedups over MADlib, StatsModels, and R. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5130
Venue
SIGMOD
Year
2016
Pagerank
0.00016135159
Overall Rank
834 | 94.20%
DOI
10.1145/2882903.2882939

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