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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)

Paper ID
12432
Venue
VLDB
Year
2021
Pagerank
4.9280116e-05
Overall Rank
6,779 | 52.85%
DOI
10.14778/3476249.3476304

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
5,313 XInsight: eXplainable Data Analysis Through The Lens of Causality 2023 SIGMOD 5.573009e-05
10,758 Stress-Testing ML Pipelines with Adversarial Data Corruption 2025 VLDB 4.1945683e-05
10,875 SDEcho: Efficient Explanation of Aggregated Sequence Difference 2025 VLDB 4.1945683e-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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