Reconstructing and Querying ML Pipeline Intermediates

Summary: Reconstructs and exposes ML pipeline intermediates as queryable, lineage-aware datasets so existing debugging and fairness tools can operate without manual pipeline rewrites. Reduces developer effort and risk of introducing analysis bugs while revealing input–output dependencies. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 476
Venue: CIDR
Year: 2023
Pagerank: 5.093636e-05
Overall Rank: 11,353 | 22.11%
DOI: -

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Authors

1. Sebastian Schelter (University of Amsterdam)

BibTeX Citation

@inproceedings{schelter_cidr23,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '23},
        title = {{Reconstructing and Querying ML Pipeline Intermediates}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Schelter, Sebastian},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

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

Rank	Cited Paper	Year	Venue	Pagerank
1,951	Interpretable Data-Based Explanations for Fairness Debugging	2022	SIGMOD	9.4252389e-05

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