@conference{Schaffland2025c,

title = {Urban Traffic Forecasting, Urban Data Platforms and Urban Foundations Models},

author = {Axel Schaffland and Julius Schöning },

year  = {2025},

date = {2025-12-01},

booktitle = {2025 7th Experiment@ International Conference (exp.at'25)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{pmlr-v269-luttermann25a,

title = {Lifted Model Construction without Normalisation: A Vectorised Approach to Exploit Symmetries in Factor Graphs},

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

editor = {Guy Wolf and Smita Krishnaswamy},

url = {https://proceedings.mlr.press/v269/luttermann25a.html

https://raw.githubusercontent.com/mlresearch/v269/main/assets/luttermann25a/luttermann25a.pdf},

year  = {2025},

date = {2025-11-26},

urldate = {2025-11-26},

booktitle = {Proceedings of the Third Learning on Graphs Conference},

volume = {269},

pages = {46:1--46:17},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

abstract = {Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes of logical variables. We found that the current state-of-the-art algorithm to construct a lifted representation in form of a parametric factor graph misses symmetries between factors that are exchangeable but scaled differently, thereby leading to a less compact representation. In this paper, we propose a generalisation of the advanced colour passing (ACP) algorithm, which is the state of the art to construct a parametric factor graph. Our proposed algorithm allows for potentials of factors to be scaled arbitrarily and efficiently detects more symmetries than the original ACP algorithm. By detecting strictly more symmetries than ACP, our algorithm significantly reduces online query times for probabilistic inference when the resulting model is applied, which we also confirm in our experiments. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ijcai2025p1009,

title = {Approximate Lifted Model Construction},

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

editor = {James Kwok},

doi = {10.24963/ijcai.2025/1009},

year  = {2025},

date = {2025-08-01},

urldate = {2025-08-01},

booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, {IJCAI-25}},

pages = {9077--9085},

publisher = {International Joint Conferences on Artificial Intelligence Organization},

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. },

note = {Main Track},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Schaffland2025b,

title = {Urban foundation models andartificial intelligence safety},

author = {Axel Schaffland and Marco Schaarschmidt and Firas Adleh and Julius Schöning},

year  = {2025},

date = {2025-02-01},

booktitle = {2025 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI)},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}