CAPS: Cost-Aware ML Pipeline Selection
Summary: CAPS adds cost-aware pipeline selection to AutoML, orthogonal to the underlying search strategy, via lightweight time/cost estimation. Models candidate pipelines as a directed hypergraph and solves a constrained prize-collecting subset problem with a greedy approximation, cutting waste up to 4x. (summarized by gpt-5.4-mini on May 27 2026)
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Authors
- 1. Antonios Kontaxakis (Universitat Politècnica de Catalunya; Université Libre de Bruxelles)
- 2. Dimitris Sacharidis (Université Libre de Bruxelles)
- 3. Alberto Abelló (Universitat Politècnica de Catalunya)
- 4. Sergi Nadal (Universitat Politècnica de Catalunya)
- 5. Alkis Simitsis (Athena Research Center)
BibTeX Citation
@article{kontaxakis_vldb26,
title = {{CAPS: Cost-Aware ML Pipeline Selection}},
author = {Kontaxakis, Antonios and Sacharidis, Dimitris and Abelló, Alberto and Nadal, Sergi and Simitsis, Alkis},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {7},
pages = {1427--1440},
doi = {10.14778/3801059.3801060},
url = {https://doi.org/10.14778/3801059.3801060},
year = {2026}
}
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