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Explaining Inference Queries with Bayesian Optimization

Summary: BOExplain treats inference-query explanation as black-box optimization, searching for predicates whose tuple removal most changes an unexpected aggregate result. Bayesian optimization, with categorical-variable encoding and warm starts, explains effects rooted in source, training, or inference data. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12620
Venue
VLDB
Year
2021
Pagerank
5.7465524e-05
Overall Rank
6,884 | 52.78%
DOI
10.14778/3476249.3476304

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{lockhart_vldb21,
        title = {{Explaining Inference Queries with Bayesian Optimization}},
        author = {Lockhart, Brandon and Peng, Jinglin and Wu, Weiyuan and Wang, Jiannan and Wu, Eugene},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {11},
        pages = {2576--2585},
        doi = {10.14778/3476249.3476304},
        url = {https://doi.org/10.14778/3476249.3476304},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
4,876 XInsight: eXplainable Data Analysis Through The Lens of Causality 2023 SIGMOD 6.4687705e-05
10,991 Stress-Testing ML Pipelines with Adversarial Data Corruption 2025 VLDB 5.093636e-05
11,098 SDEcho: Efficient Explanation of Aggregated Sequence Difference 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 16 of 16 cited papers.

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

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