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SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging
Summary: SliceLine uses monotonicity-based pruning and linear-algebra enumeration to accelerate top-K slice finding for ML model debugging. It enables exact enumeration over large, overlapping feature slices with parallelization on ML stacks, surpassing single-node memory limits.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
hb513c08b0e4a51dd
Venue
SIGMOD
Year
2021
Pagerank
8.6309237e-05
Overall Rank
2,326 | 84.37%
DOI
10.1145/3448016.3457323
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{sagadeeva_sigmod21,
title = {{SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging}},
author = {Sagadeeva, Svetlana and Boehm, Matthias},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457323},
url = {https://dl.acm.org/doi/10.1145/3448016.3457323},
year = {2021}
}
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