Approximate Lifted Model Construction
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 9077-9085.
https://doi.org/10.24963/ijcai.2025/1009
Keywords
- Uncertainty in AI: UAI: Graphical models
- Uncertainty in AI: UAI: Statistical relational AI
- Uncertainty in AI: UAI: Tractable probabilistic models
- Uncertainty in AI: UAI: Uncertainty representations
Abstract
Probabilistic relational models such as parametric factor graphs enable efficient (lifted) inference by exploiting the indistinguishability of objects. In lifted inference, a representative of indistinguishable objects is used for computations. To obtain a relational (i.e., lifted) representation, the Advanced Colour Passing (ACP) algorithm is the state of the art. The ACP algorithm, however, requires underlying distributions, encoded as potential-based factorisations, to exactly match to identify and exploit indistinguishabilities. Hence, ACP is unsuitable for practical applications where potentials learned from data inevitably deviate even if associated objects are indistinguishable. To mitigate this problem, we introduce the ε-Advanced Colour Passing (ε-ACP) algorithm, which allows for a deviation of potentials depending on a hyperparameter ε. ε-ACP efficiently uncovers and exploits indistinguishabilities that are not exact. We prove that the approximation error induced by ε-ACP is strictly bounded and our experiments show that the approximation error is close to zero in practice.
@inproceedings{ijcai2025p1009,
title = {Approximate Lifted Model Construction},
author = {Luttermann, Malte and Speller, Jan and Gehrke, Marcel and Braun, Tanya and Möller, Ralf and Hartwig, Mattis},
booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-25}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {James Kwok},
pages = {9077--9085},
year = {2025},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2025/1009},
url = {https://doi.org/10.24963/ijcai.2025/1009},
}
TY - CPAPER TI - Approximate Lifted Model Construction AU - Luttermann, Malte AU - Speller, Jan AU - Gehrke, Marcel AU - Braun, Tanya AU - Möller, Ralf AU - Hartwig, Mattis T2 - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2025 SP - 9077 EP - 9085 DO - 10.24963/ijcai.2025/1009 UR - https://doi.org/10.24963/ijcai.2025/1009 ER -Copied
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