Grouped Learning: Group-By Model Selection Workloads

Summary: Grouped Learning treats subgroup ML as group-by model selection, enabling group-level models that can improve accuracy and meet privacy/regulatory constraints. It argues for high-throughput parallel training across many groups to scale dozens-to-hundreds of models per group. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6014
Venue: SIGMOD
Year: 2021
Pagerank: 4.1945683e-05
Overall Rank: 11,447 | 20.37%
DOI: 10.1145/3448016.3450576

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Authors

1. Side Li

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
683	Cerebro: A Data System for Optimized Deep Learning Model Selection	2020	VLDB	0.00018195476
8,864	Cerebro: A Layered Data Platform for Scalable Deep Learning	2021	CIDR	4.4326439e-05

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