@inproceedings{pmlr-v246-luttermann24a,

title = {Efficient Detection of Commutative Factors in Factor Graphs},

author = {Malte Luttermann and Johann Machemer and Marcel Gehrke},

editor = {Johan Kwisthout and Silja Renooij},

url = {https://proceedings.mlr.press/v246/luttermann24a.html},

year  = {2024},

date = {2024-09-01},

booktitle = {Proceedings of The 12th International Conference on Probabilistic Graphical Models},

volume = {246},

pages = {38–56},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

abstract = {Lifted probabilistic inference exploits symmetries in probabilistic graphical models to allow for tractable probabilistic inference with respect to domain sizes. To exploit symmetries in, e.g., factor graphs, it is crucial to identify commutative factors, i.e., factors having symmetries within themselves due to their arguments being exchangeable. The current state-of-the-art to check whether a factor is commutative with respect to a subset of its arguments iterates over all possible subsets of the factor’s arguments, i.e., O($2^n$) iterations for a factor with n arguments in the worst case. In this paper, we efficiently solve the problem of detecting commutative factors in a factor graph. In particular, we introduce the detection of commutative factors (DECOR) algorithm, which allows us to drastically reduce the computational effort for checking whether a factor is commutative in practice. We prove that DECOR efficiently identifies restrictions to drastically reduce the number of required iterations and validate the efficiency of DECOR in our empirical evaluation.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Luttermann2024a,

title = {Efficient Detection of Exchangeable Factors in Factor Graphs},

author = {Malte Luttermann and Johann Machemer and Marcel Gehrke},

url = {https://journals.flvc.org/FLAIRS/article/view/135518},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the Thirty-Seventh International FLAIRS Conference (FLAIRS-2024)},

publisher = {Florida Online Journals},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Luttermann2024b,

title = {Lifted Causal Inference in Relational Domains},

author = {Malte Luttermann and Mattis Hartwig and Tanya Braun and Ralf Möller and Marcel Gehrke},

url = {https://proceedings.mlr.press/v236/luttermann24a.html},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the Third Conference on Causal Learning and Reasoning (CLeaR-2024)},

publisher = {PMLR},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Luttermann2024c,

title = {Colour Passing Revisited: Lifted Model Construction with Commutative Factors},

author = {Malte Luttermann and Tanya Braun and Ralf Möller and Marcel Gehrke},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/30034},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-2024)},

publisher = {AAAI Press},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Anderson2024,

title = {Revealing Unintentional Information Leakage in Low-Dimensional Facial Portrait Representations},

author = {Kathleen Anderson and Thomas Martinetz},

year  = {2024},

date = {2024-01-01},

booktitle = {International Conference on Artificial Neural Networks (ICANN) 2024},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-70893-0_13,

title = {Towards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models},

author = {Malte Luttermann and Ralf Möller and Mattis Hartwig},

editor = {Andreas Hotho and Sebastian Rudolph},

isbn = {978-3-031-70893-0},

year  = {2024},

date = {2024-01-01},

booktitle = {KI 2024: Advances in Artificial Intelligence},

pages = {175–189},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Probabilistic relational models provide a well-established formalism to combine first-order logic and probabilistic models, thereby allowing to represent relationships between objects in a relational domain. At the same time, the field of artificial intelligence requires increasingly large amounts of relational training data for various machine learning tasks. Collecting real-world data, however, is often challenging due to privacy concerns, data protection regulations, high costs, and so on. To mitigate these challenges, the generation of synthetic data is a promising approach. In this paper, we solve the problem of generating synthetic relational data via probabilistic relational models. In particular, we propose a fully-fledged pipeline to go from relational database to probabilistic relational model, which can then be used to sample new synthetic relational data points from its underlying probability distribution. As part of our proposed pipeline, we introduce a learning algorithm to construct a probabilistic relational model from a given relational database.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}