On the Parameterized Complexity of Learning First-Order Logic
Summary: Establishes parameterized complexity bounds for learning first-order queries: proves learning is at least as hard as FO model-checking, yielding AW[*]-hardness on general structures. Gives an FPT agnostic PAC algorithm for FO learning over nowhere-dense (sparse) data. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
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| 2,124 | Characterizing Schema Mappings via Data Examples | 2010 | PODS | 9.4912951e-05 |
| 2,750 | Learning and Verifying Quantified Boolean Queries by Example | 2013 | PODS | 8.176296e-05 |
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