Scaling Relational Inference Using Proofs and Refutations
Abstract
Many inference problems are naturally formulated using hard and soft constraints over relational domains: the desired solution must satisfy the hard constraints, while optimizing the objectives expressed by the soft constraints. Existing techniques for solving such constraints rely on efficiently grounding a sufficient subset of constraints that is tractable to solve. We present an eager-lazy grounding algorithm that eagerly exploits proofs and lazily refutes counterexamples. We show that our algorithm achieves significant speedup over existing approaches without sacrificing soundness for real-world applications from information retrieval and program analysis.
Cite
Text
Mangal et al. "Scaling Relational Inference Using Proofs and Refutations." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.10426Markdown
[Mangal et al. "Scaling Relational Inference Using Proofs and Refutations." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/mangal2016aaai-scaling/) doi:10.1609/AAAI.V30I1.10426BibTeX
@inproceedings{mangal2016aaai-scaling,
title = {{Scaling Relational Inference Using Proofs and Refutations}},
author = {Mangal, Ravi and Zhang, Xin and Kamath, Aditya and Nori, Aditya V. and Naik, Mayur},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {3278-3286},
doi = {10.1609/AAAI.V30I1.10426},
url = {https://mlanthology.org/aaai/2016/mangal2016aaai-scaling/}
}