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)
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Authors
- 1. Side Li (University of California San Diego)
BibTeX Citation
@inproceedings{li_sigmod21,
title = {{Grouped Learning: Group-By Model Selection Workloads}},
author = {Li, Side},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3450576},
url = {https://dl.acm.org/doi/10.1145/3448016.3450576},
year = {2021}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,157 | Cerebro: A Data System for Optimized Deep Learning Model Selection | 2020 | VLDB | 0.00011924049 |
| 8,979 | Cerebro: A Layered Data Platform for Scalable Deep Learning | 2021 | CIDR | 5.3399615e-05 |
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