Regularizing Conjunctive Features for Classification
Summary: Study of generating conjunctive-feature queries that linearly separate labeled entities, introducing regularizers on feature dimension, join count, and generalized hypertree width (ghw), plus approximate variants allowing bounded misclassification. Main results: separability is tractable for bounded ghw, explicit feature-generation is intractable due to potential query size, but classification can be done efficiently without materializing features under bounded ghw. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,160 | Extremal Fitting Problems for Conjunctive Queries | 2023 | PODS | 4.1905499e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 657 | An Analytical Study of Large SPARQL Query Logs | 2018 | VLDB | 0.00018581389 |
| 758 | Materialization Optimizations for Feature Selection Workloads | 2014 | SIGMOD | 0.00017053915 |
| 1,322 | Hypertree Decompositions: Questions and Answers | 2016 | PODS | 0.00012595941 |
| 6,345 | A Relational Framework for Classifier Engineering | 2017 | PODS | 5.0970607e-05 |
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