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)
Incoming Non-self Citations Over Time
Authors
- 1. Brandon Lockhart (Simon Fraser University)
- 2. Jinglin Peng (Simon Fraser University)
- 3. Weiyuan Wu (Simon Fraser University)
- 4. Jiannan Wang (Simon Fraser University)
- 5. Eugene Wu (Columbia University)
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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