Explaining Inference Queries with Bayesian Optimization
Summary: BOExplain treats inference-query explanations as black-box optimization and uses Bayesian optimization to discover input-predicate deletions that alter the target result. It introduces two techniques—individual contribution encoding and warm-start for categorical variables—and shows stronger explanations than prior engines on real data; open-source Python package. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Brandon Lockhart
- 2. Jinglin Peng
- 3. Weiyuan Wu
- 4. Jiannan Wang
- 5. Eugene Wu
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,321 | XInsight: eXplainable Data Analysis Through The Lens of Causality | 2023 | SIGMOD | 5.5676564e-05 |
| 10,764 | Stress-Testing ML Pipelines with Adversarial Data Corruption | 2025 | VLDB | 4.1905499e-05 |
| 10,879 | SDEcho: Efficient Explanation of Aggregated Sequence Difference | 2025 | VLDB | 4.1905499e-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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Semantically Similar Papers
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