Research

Institute for Artificial Intelligence (KI)

General overview of the research scope of the Institute for Artificial Intelligence.

Area

Research Area	Department
Combining discrete probability distributions and Deep Learning	AC, MLS
Computer Vision	ISP
Constrained Learning	IS
Explainable AI	AC, MLS
Generative Modeling	AC, MLS
Geometric Deep Learning	AC, MLS
Graph Machine Learning	AC, MLS
Graph Neural Networks	AC, MLS, IS
Intelligent Human-Computer Interaction	AC
Intelligent Vehicles	AC, ISP
Knowledge Graphs	AC, MLS
Machine Learning and Simulation	AC, MLS, IS
Machine Learning for Natural Language Processing	AC, MLS
Machine Learning and Combinatorial Optimization	MLS
Multimodal Human Acitivty Analysis	ISP
Open-Set-, Open-Domain- and Uncertainty-aware Recognition	ISP
Optimization	IS
Physics-based Machine Learning	AC, MLS, IS
Reflection on Web, AI and Society	AC
Reinforcement Learning	IS
Resource- and Data-efficient Recognition	ISP
Robust Machine Learning	IS
Semantic Digital Twins	AC
Signal Processing	IS
Time Series Analysis	AC

Applications

Area	Department
Applications for Health Monitoring, Physiological Analysis and Medical Diagnostics	ISP
Applications of Foundation Models	AC
Assistive Systems and Robotics	AC, ISP
Knowledge Graphs and Semantic Digital Twins for Architecture, Engineering, and Construction	AC
Knowledge Graphs and Semantic Digital Twins for Engineering Design and Production	AC
Knowledge Graphs for Intelligent Vehicles	AC
Machine Learning for (Bio-) Medical Applications	MLS
Machine Learning for Molecules (Property Prediction, ML for PDEs)	MLS
ML for Physical Systems	AC, MLS
NLP for Biomedical Applications	AC

Institute Publications

2024
1. Asma, Z., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024, May). NPCS: Native Provenance Computation for SPARQL. Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore. https://doi.org/10.1145/3589334.3645557
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  @inproceedings{zubaria2024native, abstract = {The popularity of Knowledge Graphs (KGs) both in industry and academia owes credit to their flexible data model, suitable for data integration from multiple sources. Several KG-based applications such as trust assessment or view maintenance on dynamic data rely on the ability to compute provenance explanations for query results. The how-provenance of a query result is an expression that encodes the records (triples or facts) that explain its inclusion in the result set. This article proposes NPCS, a Native Provenance Computation approach for SPARQL queries. NPCS annotates query results with their how-provenance. By building upon spm-provenance semirings, NPCS supports both monotonic and non-monotonic SPARQL queries. Thanks to its reliance on query rewriting techniques, the approach is directly applicable to already deployed SPARQL engines using different reification schemes -- including RDF-star. Our experimental evaluation on two popular SPARQL engines (GraphDB and Stardog) shows that our novel query rewriting brings a significant runtime improvement over existing query rewriting solutions, scaling to RDF graphs with billions of triples.}, author = {Asma, Zubaria and Hernández, Daniel and Galárraga, Luis and Flouris, Giorgos and Fundulaki, Irini and Hose, Katja}, booktitle = {Proceedings of the ACM Web Conference 2024 (WWW '24), May13--17, 2024, Singapore, Singapore}, doi = {10.1145/3589334.3645557}, eventdate = {May 13 -17 2024}, eventtitle = {WWW '24}, isbn = {979-8-4007-0171-9/24/05}, language = {English}, month = {05}, publisher = {ACM}, title = {NPCS: Native Provenance Computation for SPARQL}, url = {https://doi.org/10.1145/3589334.3645557}, venue = {Singapore}, year = 2024 }
2. Blomqvist, E., García-Castro, R., Hernández, D., Hitzler, P., Lindecrantz, M., & Poveda-Villalón, M. (Eds.). (2024). Proceedings of the The 2nd International Workshop on Knowledge Graphs for Sustainability (KG4S 2024) colocated with the 21st Extended Semantic Web Conference (ESWC 2024) (Vol. 3753). CEUR. https://ceur-ws.org/Vol-3753/
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  @proceedings{blomqvist2024proceedings, editor = {Blomqvist, Eva and García-Castro, Raúl and Hernández, Daniel and Hitzler, Pascal and Lindecrantz, Mikael and Poveda-Villalón, María}, eventdate = {2024-05-27}, eventtitle = {Knowledge Graphs for Sustainability 2024}, month = {05}, publisher = {CEUR}, title = {Proceedings of the The 2nd International Workshop on Knowledge Graphs for Sustainability (KG4S 2024) colocated with the 21st Extended Semantic Web Conference (ESWC 2024)}, url = {https://ceur-ws.org/Vol-3753/}, venue = {Hersonissos, Greece, May 27th, 2024}, volume = 3753, year = 2024 }
3. Elenter, J., Chamon, L. F. O., & Ribeiro, A. (2024, May). Near-Optimal Solutions of Constrained Learning Problems. Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria. https://doi.org/10.48550/arXiv.2403.11844
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  @inproceedings{elenter2024nearoptimal, abstract = {With the widespread adoption of machine learning systems, the need to curtail their behavior has become increasingly apparent. This is evidenced by recent advancements towards developing models that satisfy robustness, safety, and fairness requirements. These requirements can be imposed (with generalization guarantees) by formulating constrained learning problems that can then be tackled by dual ascent algorithms. Yet, though these algorithms converge in objective value, even in non-convex settings, they cannot guarantee that their outcome is feasible. Doing so requires randomizing over all iterates, which is impractical in virtually any modern applications. Still, final iterates have been observed to perform well in practice. In this work, we address this gap between theory and practice by characterizing the constraint violation of Lagrangian minimizers associated with optimal dual variables, despite lack of convexity. To do this, we leverage the fact that non-convex, finite-dimensional constrained learning problems can be seen as parametrizations of convex, functional problems. Our results show that rich parametrizations effectively mitigate the issue of feasibility in dual methods, shedding light on prior empirical successes of dual learning. We illustrate our findings in fair learning tasks.}, archiveprefix = {arXiv}, author = {Elenter, Juan and Chamon, Luiz F. O. and Ribeiro, Alejandro}, booktitle = {Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria}, doi = {10.48550/arXiv.2403.11844}, eprint = {2403.11844}, language = {en}, month = {05}, preprinturl = {https://arxiv.org/abs/2403.11844}, primaryclass = {cs.LG}, publisher = {ICLR}, title = {Near-Optimal Solutions of Constrained Learning Problems}, year = 2024 }
4. Elshani, D., Dervishaj, A., Hernández, D., Gudmundsson, K., Staab, S., & Wortmann, T. (2024). An Ontology for the Reuse and Tracking of Prefabricated Building Components. Proceedings of the The 2nd International Workshop on Knowledge Graphs for Sustainability (KG4S 2024) Colocated with the 21st Extended Semantic Web Conference (ESWC 2024), 3753, 53–64. https://ceur-ws.org/Vol-3753/paper5.pdf
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  @inproceedings{elshani2024ontology, author = {Elshani, Diellza and Dervishaj, Arlind and Hernández, Daniel and Gudmundsson, Kjartan and Staab, Steffen and Wortmann, Thomas}, booktitle = {Proceedings of the The 2nd International Workshop on Knowledge Graphs for Sustainability (KG4S 2024) colocated with the 21st Extended Semantic Web Conference (ESWC 2024)}, eventdate = {2024-05-27}, eventtitle = {Knowledge Graphs for Sustainability 2024}, month = {05}, pages = {53-64}, publisher = {CEUR}, title = {An Ontology for the Reuse and Tracking of Prefabricated Building Components}, url = {https://ceur-ws.org/Vol-3753/paper5.pdf}, venue = {Hersonissos, Greece, May 27th, 2024}, volume = 3753, year = 2024 }
5. Errica, F., & Niepert, M. (2024, May). Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks. Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria. https://doi.org/10.48550/arXiv.2305.10544
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  @inproceedings{errica2024tractable, abstract = {We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by vertices in the context of message-passing neural networks, we build hierarchies of sum-product networks (SPNs) where the parameters of a parent SPN are learnable transformations of the a-posterior mixing probabilities of its children's sum units. Due to weight sharing and the tree-shaped computation graphs of GSPNs, we obtain the efficiency and efficacy of deep graph networks with the additional advantages of a probabilistic model. We show the model's competitiveness on scarce supervision scenarios, under missing data, and for graph classification in comparison to popular neural models. We complement the experiments with qualitative analyses on hyper-parameters and the model's ability to answer probabilistic queries.}, archiveprefix = {arXiv}, author = {Errica, Federico and Niepert, Mathias}, booktitle = {Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria}, doi = {10.48550/arXiv.2305.10544}, eprint = {2305.10544}, language = {en}, month = {05}, preprinturl = {https://arxiv.org/abs/2305.10544v2}, primaryclass = {cs.LG}, title = {Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks}, year = 2024 }
6. Hagnberger, J., Kalimuthu, M., Musekamp, D., & Niepert, M. (2024, May). Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent PDEs. Proceedings of the AI4DifferentialEquations in Science Workshop at ICLR 2024, May 7-11, 2024, Austria.
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  @inproceedings{hagnberger2024vectorized, abstract = {Neural Operators are a recent class of data-driven models for learning solutions to Partial Differential Equations (PDEs). Traditionally, these models are trained in an autoregressive fashion using data collected at discrete time points in the evolution of the PDE. This setup gives rise to two problems: (i) poor temporal generalization due to error accumulation and (ii) poor zero-shot super-resolution capabilities. To address these issues, we propose Vectorized Conditional Neural Fields (VCNeF), a general framework that utilizes transformers and implicit neural representations to efficiently solve time-dependent PDEs of varying coefficients. A comprehensive evaluation of VCNeF on the challenging 1D and 2D PDEs from PDEBench demonstrates the superiority of our model over four state-of-the-art baselines. Furthermore, our proposed model achieves faster inference and generalizes better to unseen PDE parameters than the compared models.}, author = {Hagnberger, Jan and Kalimuthu, Marimuthu and Musekamp, Daniel and Niepert, Mathias}, booktitle = {Proceedings of the AI4DifferentialEquations in Science workshop at ICLR 2024, May 7-11, 2024, Austria}, month = {05}, publisher = {ICLR}, title = {Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent PDEs}, year = 2024 }
7. Liu, A., Niepert, M., & den Broeck, G. V. (2024, May). Image Inpainting via Tractable Steering of Diffusion Models. Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria. https://doi.org/10.48550/arXiv.2401.03349
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  @inproceedings{liu2023image, abstract = {Diffusion models are the current state of the art for generating photorealistic images. However, controlling the sampling process for constrained image generation tasks such as inpainting remains challenging since exact conditioning on such constraints is intractable. While existing methods use various techniques to approximate the constrained posterior, this paper proposes to exploit the ability of Tractable Probabilistic Models (TPMs) to exactly and efficiently compute the constrained posterior, and to leverage this signal to steer the denoising process of diffusion models. Specifically, this paper adopts a class of expressive TPMs termed Probabilistic Circuits (PCs). Building upon prior advances, we further scale up PCs and make them capable of guiding the image generation process of diffusion models. Empirical results suggest that our approach can consistently improve the overall quality and semantic coherence of in painted images across three natural image datasets (i.e., CelebA-HQ, ImageNet, and LSUN) with only ~10% additional computational overhead brought by the TPM.}, archiveprefix = {arXiv}, author = {Liu, Anji and Niepert, Mathias and den Broeck, Guy Van}, booktitle = {Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria}, doi = {10.48550/arXiv.2401.03349}, eprint = {2401.03349}, language = {en}, month = {05}, preprinturl = {https://arxiv.org/abs/2401.03349v1}, primaryclass = {cs.CV}, publisher = {ICLR}, title = {Image Inpainting via Tractable Steering of Diffusion Models}, venue = {Austria}, year = 2024 }
8. Qian, C., Manolache, A., Ahmed, K., Zeng, Z., den Broeck, G. V., Niepert, M., & Morris, C. (2024, May). Probabilistically Rewired Message-Passing Neural Networks. Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria. https://doi.org/10.48550/arXiv.2310.02156
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  @inproceedings{qian2023probabilistically, abstract = {Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input. However, they operate on a fixed input graph structure, ignoring potential noise and missing information. Furthermore, their local aggregation mechanism can lead to problems such as over-squashing and limited expressive power in capturing relevant graph structures. Existing solutions to these challenges have primarily relied on heuristic methods, often disregarding the underlying data distribution. Hence, devising principled approaches for learning to infer graph structures relevant to the given prediction task remains an open challenge. In this work, leveraging recent progress in exact and differentiable k-subset sampling, we devise probabilistically rewired MPNNs (PR-MPNNs), which learn to add relevant edges while omitting less beneficial ones. For the first time, our theoretical analysis explores how PR-MPNNs enhance expressive power, and we identify precise conditions under which they outperform purely randomized approaches. Empirically, we demonstrate that our approach effectively mitigates issues like over-squashing and under-reaching. In addition, on established real-world datasets, our method exhibits competitive or superior predictive performance compared to traditional MPNN models and recent graph transformer architectures.}, archiveprefix = {arXiv}, author = {Qian, Chendi and Manolache, Andrei and Ahmed, Kareem and Zeng, Zhe and den Broeck, Guy Van and Niepert, Mathias and Morris, Christopher}, booktitle = {Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria}, doi = {10.48550/arXiv.2310.02156}, eprint = {2310.02156}, language = {en}, month = {05}, preprinturl = {https://arxiv.org/abs/2310.02156}, primaryclass = {cs.LG}, publisher = {ICLR}, title = {Probabilistically Rewired Message-Passing Neural Networks}, year = 2024 }
9. Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024, May). From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries. Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore. https://doi.org/10.1145/3589334.3645550
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  @inproceedings{seifer2024shapes, abstract = {SPARQL CONSTRUCT queries allow for the specification of data processing pipelines that transform given input graphs into new output graphs. It is now common to constrain graphs through SHACL shapes allowing users to understand which data they can expect and which not. However, it becomes challenging to understand what graph data can be expected at the end of a data processing pipeline without knowing the particular input data: Shape constraints on the input graph may affect the output graph, but may no longer apply literally, and new shapes may be imposed by the query template. In this paper, we study the derivation of shape constraints that hold on all possible output graphs of a given SPARQL CONSTRUCT query. We assume that the SPARQL CONSTRUCT query is fixed, e.g., being part of a program, whereas the input graphs adhere to input shape constraints but may otherwise vary over time and, thus, are mostly unknown. We study a fragment of SPARQL CONSTRUCT queries (SCCQ) and a fragment of SHACL (Simple SHACL). We formally define the problem of deriving the most restrictive set of Simple SHACL shapes that constrain the results from evaluating a SCCQ over any input graph restricted by a given set of Simple SHACL shapes. We propose and implement an algorithm that statically analyses input SHACL shapes and CONSTRUCT queries and prove its soundness and complexity.}, author = {Seifer, Philipp and Hernández, Daniel and Lämmel, Ralf and Staab, Steffen}, booktitle = {Proceedings of the ACM Web Conference 2024 (WWW '24), May13--17, 2024, Singapore, Singapore}, doi = {10.1145/3589334.3645550}, eventdate = {May 13–17, 2024}, eventtitle = {WWW '24}, isbn = {979-8-4007-0171-9/24/05}, month = {05}, preprinturl = {https://arxiv.org/abs/2402.08509}, publisher = {ACM}, title = {From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries}, url = {https://doi.org/10.1145/3589334.3645550}, venue = {Singapore}, year = 2024 }
10. Tran, H.-C., Nguyen, D. M. H., Nguyen, M.-D., Le, N. H., & T. Nguyen, B. (2024, May). Energy Minimizing-based Token Merging for Accelerating Transformers. Proceedings of Practical ML for Low Resource Settings in Science Workshop at ICLR 2024, May 7-11, 2024, Austria.
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  @inproceedings{tran2024energy, abstract = {Model compression has been an active research field that has been used to reduce the size and complexity of the model. In a recent noteworthy study, ToMe and its variants utilize the Bipartite Soft Matching (BSM) algorithm in which tokens representing patches in an image are split into two sets, and top-k similar tokens from one set are merged. This approach utilizes pre-trained weights, enhances speed, and reduces memory usage. However, these algorithms have some drawbacks. First, the choice of a token-splitting strategy significantly influences algorithm performance since tokens in one set can only perceive tokens in the other set, leading to mis-merging issues. Furthermore, although ToMe is effective in the initial layers, it becomes increasingly problematic in deeper layers as the number of tokens diminishes because of damaged informative tokens. To address these limitations, rather than relying on specific splitting strategies like BSM, we propose a new algorithm called PiToMe. Specifically, we prioritize the protection of informative tokens using an additional factor called energy score. In experiments, PiToMe achieved up to a 50% memory reduction while exhibiting superior off-the-shelf performance on image classification ( keeping a 1.71% average performance drop compared to 2.6% for ToMe) and image-text retrieval (1.35% average performance drop compared to 6.89% for ToMe) compared to previous BSM-based approaches dependent solely on token similarity.}, author = {Tran, Hoai-Chau and Nguyen, Duy Minh Ho and Nguyen, Manh-Duy and Le, Ngan Hoang and T. Nguyen, Binh}, booktitle = {Proceedings of Practical ML for Low Resource Settings in Science workshop at ICLR 2024, May 7-11, 2024, Austria}, language = {en}, month = {05}, publisher = {ICLR}, title = {Energy Minimizing-based Token Merging for Accelerating Transformers}, year = 2024 }
11. Zubaria, A., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024, May). NPCS: Native Provenance Computation for SPARQL. Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore. https://doi.org/10.1145/3589334.3645557
  - BibTeX
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  @conference{zubaria2024native, abstract = {The popularity of Knowledge Graphs (KGs) both in industry and academia owes credit to their flexible data model, suitable for data integration from multiple sources. Several KG-based applications such as trust assessment or view maintenance on dynamic data rely on the ability to compute provenance explanations for query results. The how-provenance of a query result is an expression that encodes the records (triples or facts) that explain its inclusion in the result set. This article proposes NPCS, a Native Provenance Computation approach for SPARQL queries. NPCS annotates query results with their how-provenance. By building upon spm-provenance semirings, NPCS supports both monotonic and non-monotonic SPARQL queries. Thanks to its reliance on query rewriting techniques, the approach is directly applicable to already deployed SPARQL engines using different reification schemes -- including RDF*. Our experimental evaluation on two popular SPARQL engines (GraphDB and Stardog) shows that our novel query rewriting brings a significant runtime improvement over existing query rewriting solutions, scaling to RDF graphs with billions of triples.}, author = {Zubaria, Asma and Hernández, Daniel and Galárraga, Luis and Flouris, Giorgos and Fundulaki, Irini and Hose, Katja}, booktitle = {Proceedings of the ACM Web Conference 2024 (WWW '24), May13--17, 2024, Singapore, Singapore}, doi = {10.1145/3589334.3645557}, eventdate = {May 13 -17 2024}, eventtitle = {WWW '24}, language = {English}, month = {05}, publisher = {ACM}, title = {NPCS: Native Provenance Computation for SPARQL}, url = {https://doi.org/10.1145/3589334.3645557}, venue = {Singapore}, year = 2024 }
12. Asma, Z., Hernandez, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024). Code and benchmark for NPCS, a Native Provenance Computation for SPARQL. https://doi.org/10.18419/darus-3973
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  @misc{asma2024benchmark, abstract = {Code for the implementation and benchmark of NPCS, a Native Provenance Computation for SPARQL.The code in this dataset includes the implementation of the NPCS system, which is a middleware for SPARQL endpoints that rewrites queries to queries that annotate answers with provenance polynomials (i.e., how-provenance data). The translation rules implemented for the query rewriting can be seen in the paper.Also, the code contains scripts that include scripts and services to automatize the query execution.We use GraphDB (version 10.2.0) and Stardog (version 9.1.0) for the SPARQL endpoints. Because of the license restrictions, these software products cannot be included in this dataset and must be downloaded from the respective vendors. Also, the data must be loaded using the respective bulk loaders of GraphDB and Stardog.The datasets used in the experiments can be generated synthetic dataset generator of the WatDiv benchmark. The Wikidata dataset corresponds to the full RDF dump from May 22, 2023.Do not hesitate to contact the authors for any inquiries. }, affiliation = {Asma, Zubaria/FORT-ICS, Hernández, Daniel/University of Stuttgart, Galárraga, Luis/INRIA, Flouris, Giorgos/FORTH-ICS, Fundulaki, Irini/FORTH-ICS, Hose, Katja/TU Wien}, author = {Asma, Zubaria and Hernandez, Daniel and Galárraga, Luis and Flouris, Giorgos and Fundulaki, Irini and Hose, Katja}, doi = {10.18419/darus-3973}, howpublished = {Software}, note = {Related to: A. Zubaria, D. Hernández, L. Galárraga, G. Flouris, I. Fundulaki, and K. Hose. NPCS: Native Provenance Computation for SPARQL. Proceedings of the ACM Web Conference 2024 (WWW '24), May13-17, 2024, Singapore, Singapore, ACM, (May 2024). doi: 10.1145/3589334.3645557}, orcid-numbers = {Asma, Zubaria/0000-0002-9402-7487, Hernández, Daniel/0000-0002-7896-0875, Galárraga, Luis/0000-0002-0241-5379, Flouris, Giorgos/0000-0002-8937-4118, Fundulaki, Irini/0000-0002-4812-9896, Hose, Katja/0000-0001-7025-8099}, title = {Code and benchmark for NPCS, a Native Provenance Computation for SPARQL}, url = {https://doi.org/10.18419/darus-3973}, year = 2024 }
13. Ding, Z., Cai, H., Wu, J., Ma, Y., Liao, R., Xiong, B., & Tresp, V. (2024). zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models. Annual Conference of the North American Chapter of the Association for Computational Linguistics. https://arxiv.org/abs/2311.10112
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  @article{ding2023zeroshot, abbr = {NAACL}, arxiv = {2311.10112}, author = {Ding, Zifeng and Cai, Heling and Wu, Jingpei and Ma, Yunpu and Liao, Ruotong and Xiong, Bo and Tresp, Volker}, journal = {Annual Conference of the North American Chapter of the Association for Computational Linguistics}, title = {zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models}, url = {https://arxiv.org/abs/2311.10112}, year = 2024 }
14. Hagnberger, J., Kalimuthu, M., Musekamp, D., & Niepert, M. (2024). Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations. In Proceedings of the 41st International Conference on Machine Learning (ICML 2024). https://arxiv.org/abs/2406.03919
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  @inproceedings{hagnberger2024vectorizedconditionalneuralfields, abstract = {Transformer models are increasingly used for solving Partial Differential Equations (PDEs). Several adaptations have been proposed, all of which suffer from the typical problems of Transformers, such as quadratic memory and time complexity. Furthermore, all prevalent architectures for PDE solving lack at least one of several desirable properties of an ideal surrogate model, such as (i) generalization to PDE parameters not seen during training, (ii) spatial and temporal zero-shot super-resolution, (iii) continuous temporal extrapolation, (iv) support for 1D, 2D, and 3D PDEs, and (v) efficient inference for longer temporal rollouts. To address these limitations, we propose Vectorized Conditional Neural Fields (VCNeFs), which represent the solution of time-dependent PDEs as neural fields. Contrary to prior methods, however, VCNeFs compute, for a set of multiple spatio-temporal query points, their solutions in parallel and model their dependencies through attention mechanisms. Moreover, VCNeF can condition the neural field on both the initial conditions and the parameters of the PDEs. An extensive set of experiments demonstrates that VCNeFs are competitive with and often outperform existing ML-based surrogate models.}, archiveprefix = {arXiv}, author = {Hagnberger, Jan and Kalimuthu, Marimuthu and Musekamp, Daniel and Niepert, Mathias}, booktitle = {In Proceedings of the 41st International Conference on Machine Learning (ICML 2024)}, eprint = {2406.03919}, language = {en}, primaryclass = {cs.LG}, title = {Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations}, url = {https://arxiv.org/abs/2406.03919}, year = 2024 }
15. He, Y., Hernandez, D., Nayyeri, M., Xiong, B., Zhu, Y., Kharlamov, E., & Staab, S. (2024). Generating SROI^- Ontologies via Knowledge Graph Query Embedding Learning. https://arxiv.org/abs/2407.09212
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  @preprint{he2024generatingsroiontologiesknowledge, archiveprefix = {arXiv}, author = {He, Yunjie and Hernandez, Daniel and Nayyeri, Mojtaba and Xiong, Bo and Zhu, Yuqicheng and Kharlamov, Evgeny and Staab, Steffen}, eprint = {2407.09212}, primaryclass = {cs.AI}, title = {Generating SROI^{-} Ontologies via Knowledge Graph Query Embedding Learning}, url = {https://arxiv.org/abs/2407.09212}, year = 2024 }
16. Nguyen, D. M. H., Le, A. T., Nguyen, T. Q., Diep, N. T., Nguyen, T., Duong-Tran, D., Peters, J., Shen, L., Niepert, M., & Sonntag, D. (2024). Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model. Proceedings of Machine Learning Research. https://arxiv.org/abs/2407.04489
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  @inproceedings{nguyen2024dudedualdistributionawarecontext, abstract = {Prompt learning methods are gaining increasing attention due to their ability to customize large vision-language models to new domains using pre-trained contextual knowledge and minimal training data. However, existing works typically rely on optimizing unified prompt inputs, often struggling with fine-grained classification tasks due to insufficient discriminative attributes. To tackle this, we consider a new framework based on a dual context of both domain-shared and class-specific contexts, where the latter is generated by Large Language Models (LLMs) such as GPTs. Such dual prompt methods enhance the model's feature representation by joining implicit and explicit factors encoded in LLM knowledge. Moreover, we formulate the Unbalanced Optimal Transport (UOT) theory to quantify the relationships between constructed prompts and visual tokens. Through partial matching, UOT can properly align discrete sets of visual tokens and prompt embeddings under different mass distributions, which is particularly valuable for handling irrelevant or noisy elements, ensuring that the preservation of mass does not restrict transport solutions. Furthermore, UOT's characteristics integrate seamlessly with image augmentation, expanding the training sample pool while maintaining a reasonable distance between perturbed images and prompt inputs. Extensive experiments across few-shot classification and adapter settings substantiate the superiority of our model over current state-of-the-art baselines.}, archiveprefix = {arXiv}, author = {Nguyen, Duy M. H. and Le, An T. and Nguyen, Trung Q. and Diep, Nghiem T. and Nguyen, Tai and Duong-Tran, Duy and Peters, Jan and Shen, Li and Niepert, Mathias and Sonntag, Daniel}, booktitle = {Proceedings of Machine Learning Research}, eprint = {2407.04489}, language = {en}, primaryclass = {cs.CV}, title = {Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model}, url = {https://arxiv.org/abs/2407.04489}, venue = {Asian Conference on Machine Learning, ACML 2024}, year = 2024 }
17. Nguyen, D. M. H., Lukashina, N., Nguyen, T., Le, A. T., Nguyen, T., Ho, N., Peters, J., Sonntag, D., Zaverkin, V., & Niepert, M. (2024). Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks. In Proceedings of the 41st International Conference on Machine Learning (ICML 2024). https://arxiv.org/abs/2402.01975
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  @inproceedings{nguyen2024structureawaree3invariantmolecularconformer, abstract = {A molecule's 2D representation consists of its atoms, their attributes, and the molecule's covalent bonds. A 3D (geometric) representation of a molecule is called a conformer and consists of its atom types and Cartesian coordinates. Every conformer has a potential energy, and the lower this energy, the more likely it occurs in nature. Most existing machine learning methods for molecular property prediction consider either 2D molecular graphs or 3D conformer structure representations in isolation. Inspired by recent work on using ensembles of conformers in conjunction with 2D graph representations, we propose E(3)-invariant molecular conformer aggregation networks. The method integrates a molecule's 2D representation with that of multiple of its conformers. Contrary to prior work, we propose a novel 2D-3D aggregation mechanism based on a differentiable solver for the Fused Gromov-Wasserstein Barycenter problem and the use of an efficient conformer generation method based on distance geometry. We show that the proposed aggregation mechanism is E(3) invariant and propose an efficient GPU implementation. Moreover, we demonstrate that the aggregation mechanism helps to significantly outperform state-of-the-art molecule property prediction methods on established datasets.}, archiveprefix = {arXiv}, author = {Nguyen, Duy M. H. and Lukashina, Nina and Nguyen, Tai and Le, An T. and Nguyen, TrungTin and Ho, Nhat and Peters, Jan and Sonntag, Daniel and Zaverkin, Viktor and Niepert, Mathias}, booktitle = {In Proceedings of the 41st International Conference on Machine Learning (ICML 2024)}, eprint = {2402.01975}, language = {en}, primaryclass = {cs.LG}, title = {Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks}, url = {https://arxiv.org/abs/2402.01975}, year = 2024 }
18. Pan, J., Nayyeri, M., Li, Y., & Staab, S. (2024). HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces. Thirty-Eighth Conference on Artificial Intelligence, AAAI, 2024, Vancouver, Canada, February 22 – February 25, 2024,.
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  @inproceedings{pan2023hge, abstract = {Temporal knowledge graphs represent temporal facts (s,p,o,τ) relating a subject s and an object o via a relation label p at time τ, where τ could be a time point or time interval. Temporal knowledge graphs may exhibit static temporal patterns at distinct points in time and dynamic temporal patterns between different timestamps. In order to learn a rich set of static and dynamic temporal patterns and apply them for inference, several embedding approaches have been suggested in the literature. However, as most of them resort to single underlying embedding spaces, their capability to model all kinds of temporal patterns was severely limited by having to adhere to the geometric property of their one embedding space. We lift this limitation by an embedding approach that maps temporal facts into a product space of several heterogeneous geometric subspaces with distinct geometric properties, i.e.\ Complex, Dual, and Split-complex spaces. In addition, we propose a temporal-geometric attention mechanism to integrate information from different geometric subspaces conveniently according to the captured relational and temporal information. Experimental results on standard temporal benchmark datasets favorably evaluate our approach against state-of-the-art models.}, author = {Pan, Jiaxin and Nayyeri, Mojtaba and Li, Yinan and Staab, Steffen}, booktitle = {Thirty-eighth Conference on Artificial Intelligence, {AAAI}, 2024, Vancouver, Canada, February 22 – February 25, 2024,}, eventdate = {22 February 2024 – 25 February 2024}, preprinturl = {https://arxiv.org/abs/2312.13680}, title = {HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces}, year = 2024 }
19. Pan, X., Hernández, D., Seifer, P., Lämmel, R., & Staab, S. (2024). eSPARQL: Representing and Reconciling Agnostic and Atheistic Beliefs in RDF-star Knowledge Graphs. https://arxiv.org/abs/2407.21483
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  @preprint{pan2024esparqlrepresentingreconcilingagnostic, archiveprefix = {arXiv}, author = {Pan, Xiny and Hernández, Daniel and Seifer, Philipp and Lämmel, Ralf and Staab, Steffen}, eprint = {2407.21483}, primaryclass = {cs.AI}, title = {eSPARQL: Representing and Reconciling Agnostic and Atheistic Beliefs in RDF-star Knowledge Graphs}, url = {https://arxiv.org/abs/2407.21483}, year = 2024 }
20. Peng, K., Yin, C., Zheng, J., Liu, R., Schneider, D., Zhang, J., Yang, K., Sarfraz, M. S., Stiefelhagen, R., & Roitberg, A. (2024). Navigating Open Set Scenarios for Skeleton-based Action Recognition. The 38th Annual AAAI Conference on Artificial Intelligence. https://arxiv.org/abs/2312.06330
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  @article{peng2023navigating, author = {Peng, Kunyu and Yin, Cheng and Zheng, Junwei and Liu, Ruiping and Schneider, David and Zhang, Jiaming and Yang, Kailun and Sarfraz, M Saquib and Stiefelhagen, Rainer and Roitberg, Alina}, booktitle = {The 38th Annual AAAI Conference on Artificial Intelligence}, title = {Navigating Open Set Scenarios for Skeleton-based Action Recognition}, url = {https://arxiv.org/abs/2312.06330}, year = 2024 }
21. Potyka, N., Zhu, Y., He, Y., Kharlamov, E., & Staab, S. (2024). Robust Knowledge Extraction from Large Language Models using Social Choice Theory. In Proceedings of the 23rd International Conference on Autonomous Agents and Multi-Agent Systems. https://arxiv.org/abs/2312.14877
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  @inproceedings{noauthororeditor2023robust, abstract = {Large-language models (LLMs) have the potential to support a wide range of applications like conversational agents, creative writing, text improvement, and general query answering. However, they are ill-suited for query answering in high-stake domains like medicine because they generate answers at random and their answers are typically not robust - even the same query can result in different answers when prompted multiple times. In order to improve the robustness of LLM queries, we propose using ranking queries repeatedly and to aggregate the queries using methods from social choice theory. We study ranking queries in diagnostic settings like medical and fault diagnosis and discuss how the Partial Borda Choice function from the literature can be applied to merge multiple query results. We discuss some additional interesting properties in our setting and evaluate the robustness of our approach empirically.}, author = {Potyka, Nico and Zhu, Yuqicheng and He, Yunjie and Kharlamov, Evgeny and Staab, Steffen}, booktitle = {In Proceedings of the 23rd International Conference on Autonomous Agents and Multi-Agent Systems}, title = {Robust Knowledge Extraction from Large Language Models using Social Choice Theory}, url = {https://arxiv.org/abs/2312.14877}, year = 2024 }
22. Schwindt, S., Meisinger, L., Negreiros, B., Schneider, T., & Nowak, W. (2024). Transfer learning achieves high recall for object classification in fluvial environments with limited data. Geomorphology, 455, 109185. https://doi.org/10.1016/j.geomorph.2024.109185
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  @inproceedings{SCHWINDT2024109185, abstract = {Field surveys to collect data from fluvial ecosystems traditionally focus on specific phenomena related to geomorphology or hydrology. Low-cost unmanned aerial vehicles (UAVs) additionally empower the fast and massive collection of airborne photogrammetry, providing geospatially explicit information. This remote sensing data complements field surveys by offering contextual information on geomorphological conditions, including digital terrain models. AI-based image recognition can augment contextual information to extrapolate archetypal object classes through name labels, such as “gravel”, “sand”, “plant”, or “large wood”. However, obtaining sufficient ground truth data for these classifications, particularly in morphodynamic fluvial environments, is challenging and induces high costs. This study introduces a transfer learning approach to address the challenge of low data availability, enabling AI-based mapping of complex objects in fluvial landscapes. We leverage the learned general structure of a deep convolutional neural network (CNN) pre-trained on a broad range of images. The fixed latent features of the pre-trained CNN stem from GoogLeNet. A fixed feature extractor serves to classify objects with limited data amounts. Satisfactory performance is measured with a recall rate, expressing the ability of a model to find all occurrences of a class on an image. High spatial heterogeneity in the locations of measurements on the x-y plane improves model performance. With a minimum of 400 labeled instances, the model achieves a satisfactory 93.75-% recall for a “large wood” target class, providing evidence of the effectiveness of transfer learning in remote sensing for geomorphological studies. This ability to detect large woods in river environments is critical to restoration efforts as it helps create fish habitat, which is essential to supporting biodiversity.}, author = {Schwindt, Sebastian and Meisinger, Lisa and Negreiros, Beatriz and Schneider, Tim and Nowak, Wolfgang}, doi = {https://doi.org/10.1016/j.geomorph.2024.109185}, issn = {0169-555X}, journal = {Geomorphology}, language = {en}, pages = 109185, title = {Transfer learning achieves high recall for object classification in fluvial environments with limited data}, url = {https://www.sciencedirect.com/science/article/pii/S0169555X24001351}, volume = 455, year = 2024 }
23. Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024). Code for From Shapes to Shapes. https://doi.org/10.18419/darus-3977
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  @misc{seifer2024shapes, abstract = {This dataset contains the implementation code for an algorithm to infer SHACL shapes that the graph returned by an SPARQL CONSTRUCT query must satisfy if the input satisfies a given set of SHACL shapes. This dataset also includes an evaluation for the algorithm. The algorithm implemented in this dataset is proposed in the paper From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries. To execute the code, follow the instructions in the README.md file. For more info, please check the paper, and please have no hesitation to contact the authors for any inquiries. }, affiliation = {Seifer, Philipp/University of Koblenz, Hernández, Daniel/University of Stuttgart, Lämmel, Ralf/University of Koblenz, Staab, Steffen/University of Stuttgart}, author = {Seifer, Philipp and Hernández, Daniel and Lämmel, Ralf and Staab, Steffen}, doi = {10.18419/darus-3977}, howpublished = {Software}, note = {Related to: Philipp Seifer, Daniel Hernández, Ralf Lämmel, and Steffen Staab. 2024. From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries. In Proceedings of the ACM Web Conference 2024 (WWW ’24). ACM. doi: 10.1145/3589334.3645550}, orcid-numbers = {Seifer, Philipp/0000-0002-7421-2060, Hernández, Daniel/0000-0002-7896-0875, Lämmel, Ralf/0000-0001-9946-4363, Staab, Steffen/0000-0002-0780-4154}, title = {Code for From Shapes to Shapes}, year = 2024 }
24. Serra, G., & Niepert, M. (2024). L2XGNN: Learning to Explain Graph Neural Networks. Machine Learning Journal. https://arxiv.org/abs/2209.14402
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  @inproceedings{serra2024l2xgnnlearningexplaingraph, abstract = {Graph Neural Networks (GNNs) are a popular class of machine learning models. Inspired by the learning to explain (L2X) paradigm, we propose L2XGNN, a framework for explainable GNNs that provides faithful explanations by design. L2XGNN learns a mechanism for selecting explanatory subgraphs (motifs), which are exclusively used in the GNN message-passing operations. L2XGNN can select, for each input graph, a subgraph with specific properties, such as being sparse and connected. Imposing such constraints on the motifs often leads to more interpretable and effective explanations. Experiments on several datasets suggest that L2XGNN achieves the same classification accuracy as baseline methods using the entire input graph while ensuring that only the provided explanations are used to make predictions. Moreover, we show that L2XGNN can identify motifs responsible for the graph's properties it is intended to predict.}, archiveprefix = {arXiv}, author = {Serra, Giuseppe and Niepert, Mathias}, booktitle = {Proceedings of the European Conference on Machine Learning(ECML 2024)}, eprint = {2209.14402}, journal = {Machine Learning Journal}, language = {en}, primaryclass = {cs.LG}, title = {L2XGNN: Learning to Explain Graph Neural Networks}, url = {https://arxiv.org/abs/2209.14402}, year = 2024 }
25. Tan, Y., Lv, H., Zhou, Z., Guo, W., Xiong, B., Liu, W., Chen, C., Wang, S., & Yang, C. (2024). Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation. The 40th IEEE International Conference on Data Engineering. http://www.cs.emory.edu/~jyang71/files/logirec.pdf
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  @article{Yanchao2024LogicR, abbr = {ICDE}, author = {Tan, Yanchao and Lv, Hang and Zhou, Zihao and Guo, Wenzhong and Xiong, Bo and Liu, Weiming and Chen, Chaochao and Wang, Shiping and Yang, Carl}, journal = {The 40th IEEE International Conference on Data Engineering}, title = {Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation}, url = {http://www.cs.emory.edu/~jyang71/files/logirec.pdf}, year = 2024 }
26. Xiong, B., Nayyeri, M., Luo, L., Wang, Z., Pan, S., & Staab, S. (2024). NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning. The 38th Annual AAAI Conference on Artificial Intelligence. https://arxiv.org/abs/2312.09219
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  @inproceedings{xiong_neste_aaai24, author = {Xiong, Bo and Nayyeri, Mojtaba and Luo, Linhao and Wang, Zihao and Pan, Shirui and Staab, Steffen}, booktitle = {The 38th Annual AAAI Conference on Artificial Intelligence}, title = {NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning}, url = {https://arxiv.org/abs/2312.09219}, year = 2024 }
27. Zaverkin, V., Holzmüller, D., Christiansen, H., Errica, F., Alesiani, F., Takamoto, M., Niepert, M., & Kästner, J. (2024). Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials. Npj Comput. Mater., 10(1), Article 1. https://doi.org/10.1038/s41524-024-01254-1
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  @article{zaverkin2024uncertaintybiased, abstract = {Efficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses biased or unbiased molecular dynamics (MD) to generate candidate pools, aims to address this objective. Existing biased and unbiased MD-simulation methods, however, are prone to miss either rare events or extrapolative regions—areas of the configurational space where unreliable predictions are made. This work demonstrates that MD, when biased by the MLIP’s energy uncertainty, simultaneously captures extrapolative regions and rare events, which is crucial for developing uniformly accurate MLIPs. Furthermore, exploiting automatic differentiation, we enhance bias-forces-driven MD with the concept of bias stress. We employ calibrated gradient-based uncertainties to yield MLIPs with similar or, sometimes, better accuracy than ensemble-based methods at a lower computational cost. Finally, we apply uncertainty-biased MD to alanine dipeptide and MIL-53(Al), generating MLIPs that represent both configurational spaces more accurately than models trained with conventional MD.}, author = {Zaverkin, Viktor and Holzmüller, David and Christiansen, Henrik and Errica, Federico and Alesiani, Francesco and Takamoto, Makoto and Niepert, Mathias and Kästner, Johannes}, doi = {10.1038/s41524-024-01254-1}, issn = {20573960}, journal = {npj Comput. Mater.}, number = 1, pages = {83--}, publisher = {Springer Nature}, refid = {Zaverkin2024}, title = {Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials}, type = {Publication}, url = {https://doi.org/10.1038/s41524-024-01254-1}, volume = 10, year = 2024 }
2023
1. Baier, A., Aspandi, D., & Staab, S. (2023, August). ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23. https://opencms.uni-stuttgart.de/permalink/7e4a70e2-10ca-11ee-ba91-000e0c3db68b.pdf
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  @inproceedings{Baier2023, abstract = {Multistep prediction models are essential for the simulation and model-predictive control of dynamical systems. Verifying the safety of such models is a multi-faceted problem requiring both system-theoretic guarantees as well as establishing trust with human users. In this work, we propose a novel approach, ReLiNet (Recurrent Linear Parameter Varying Network), to ensure safety for multistep prediction of dynamical systems. Our approach simplifies a recurrent neural network to a switched linear system that is constrained to guarantee exponential stability, which acts as a surrogate for safety from a system-theoretic perspective. Furthermore, ReLiNet’s computation can be reduced to a single linear model for each time step, resulting in predictions that are explainable by definition, thereby establishing trust from a human-centric perspective. Our quantitative experiments show that ReLiNet achieves prediction accuracy comparable to that of state-of-the-art recurrent neural networks, while achieving more faithful and robust explanations compared to the model-agnostic explanation method of LIME.}, author = {Baier, Alexandra and Aspandi, Decky and Staab, Steffen}, booktitle = {Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23}, month = {08}, note = {Main Track}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, title = {ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks}, url = {https://opencms.uni-stuttgart.de/permalink/7e4a70e2-10ca-11ee-ba91-000e0c3db68b.pdf}, year = 2023 }
2. Hedeshy, R., Menges, R., & Staab, S. (2023). CNVVE: Dataset and Benchmark for Classifying Non-verbal Voice Expressions. Proc. INTERSPEECH 2023, 1553–1557. https://doi.org/10.21437/Interspeech.2023-201
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  @inproceedings{hedeshy2023cnvve, abstract = {Non-verbal voice expressions (NVVEs) have been adopted as a means of human-computer interaction in research studies. However, exploring non-verbal voice-based interactions has been constrained by the limited availability of suitable training data and computational methods for classifying such expressions, leading to a focus on simple binary inputs. We address this issue with a new dataset containing 950 audio samples comprising 6 classes of voice expressions. The data were collected from 42 speakers who donated voice recordings. The classifier was trained on the data using features derived from mel-spectrograms. Furthermore, we studied the effectiveness of data augmentation and improved over the baseline model accuracy significantly with a test accuracy of 96.6% in a 5-fold cross-validation. We have made CNVVE publicly accessible in the hope that it will serve as a benchmark for future research.}, author = {Hedeshy, Ramin and Menges, Raphael and Staab, Steffen}, booktitle = {Proc. INTERSPEECH 2023}, doi = {10.21437/Interspeech.2023-201}, eventdate = {August 20-24}, eventtitle = {Interspeech 2023}, month = {08}, pages = {1553-1557}, title = {CNVVE: Dataset and Benchmark for Classifying Non-verbal Voice Expressions}, url = {https://www.isca-speech.org/archive/interspeech_2023/hedeshy23_interspeech.html}, year = 2023 }
3. Liu, A., Niepert, M., & den Broeck, G. V. (2023, May). Image Inpainting via Tractable Steering of Diffusion Models. Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria. https://doi.org/10.48550/arXiv.2401.03349
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  @inproceedings{liu2023image, abstract = {Diffusion models are the current state of the art for generating photorealistic images. However, controlling the sampling process for constrained image generation tasks such as inpainting remains challenging since exact conditioning on such constraints is intractable. While existing methods use various techniques to approximate the constrained posterior, this paper proposes to exploit the ability of Tractable Probabilistic Models (TPMs) to exactly and efficiently compute the constrained posterior, and to leverage this signal to steer the denoising process of diffusion models. Specifically, this paper adopts a class of expressive TPMs termed Probabilistic Circuits (PCs). Building upon prior advances, we further scale up PCs and make them capable of guiding the image generation process of diffusion models. Empirical results suggest that our approach can consistently improve the overall quality and semantic coherence of in painted images across three natural image datasets (i.e., CelebA-HQ, ImageNet, and LSUN) with only ~10% additional computational overhead brought by the TPM.}, archiveprefix = {arXiv}, author = {Liu, Anji and Niepert, Mathias and den Broeck, Guy Van}, booktitle = {Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria}, doi = {10.48550/arXiv.2401.03349}, eprint = {2401.03349}, language = {en}, month = {05}, preprinturl = {https://arxiv.org/abs/2401.03349v1}, primaryclass = {cs.CV}, publisher = {ICLR}, title = {Image Inpainting via Tractable Steering of Diffusion Models}, venue = {Austria}, year = 2023 }
4. Baier, A., Aspandi, D., & Staab, S. (2023). Supplements for “ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks”". DaRUS. https://doi.org/10.18419/DARUS-3457
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  @dataset{https://doi.org/10.18419/darus-3457, abstract = {This repository contains the necessary scripts to reproduce the results from our paper "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks". See the README file for more information. The most current version of this software is available on Github.}, author = {Baier, Alexandra and Aspandi, Decky and Staab, Steffen}, doi = {10.18419/DARUS-3457}, publisher = {DaRUS}, title = {Supplements for "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks""}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3457}, year = 2023 }
5. Baier, A., & Frank, D. (2023). deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning. DaRUS. https://doi.org/10.18419/DARUS-3455
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  @dataset{https://doi.org/10.18419/darus-3455, abstract = {deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods. The toolkit is easy to use. After you follow the instructions in the README, you will be able to download a dataset, run hyperparameter optimization and identify your best-performing multistep prediction models with just three commands. The most current version of this software is available on GitHub.}, author = {Baier, Alexandra and Frank, Daniel}, doi = {10.18419/DARUS-3455}, publisher = {DaRUS}, title = {deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3455}, year = 2023 }
6. Elshani, D., Hernandez, D., Lombardi, A., Siriwardena, L., Schwinn, T., Fisher, A., Staab, S., Menges, A., & Wortmann, T. (2023). Building Information Validation and Reasoning Using Semantic Web Technologies. In M. Turrin, C. Andriotis, & A. Rafiee (Eds.), Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries (pp. 470--484). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-37189-9_31
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  @inproceedings{10.1007/978-3-031-37189-9_31, abstract = {The integration of data from various disciplines, including requirements and regulations, is essential in the co-design of buildings. Constraints that arise during design, fabrication, or construction are mainly considered in the later stages of the design process. This often leads to costly revisions during fabrication or construction. While there are some works proposing Semantic Web approaches or ad-hoc methods to identify constraint violations in the early stages of design, they mainly rely on data derived from monolithic data schemas. This paper presents a Semantic Web approach that validates and checks building data derived from federated data schemas. The proposed approach is exemplified through the design process of a segmented timber shell, a complex structure requiring negotiations across architectural, engineering, and fabrication parameters. The Building Habitat object Models (BHoM) framework is used to represent federated building data, and the approach enables a seamless movement between the graph environment and object models to support the design process. Additionally, the study discusses various technologies that aid in knowledge inference or data validation. The results of this study suggest that the use of Semantic Web technologies in conjunction with federated data schemas has significant potential to enhance co-design processes in the building industry. While further research is needed to assess its effectiveness in other scenarios, our application of this approach in checking and validating building data for a complex segmented timber shell structure demonstrates its promise as a means to streamline the design process.}, address = {Cham}, author = {Elshani, Diellza and Hernandez, Daniel and Lombardi, Alessio and Siriwardena, Lasath and Schwinn, Tobias and Fisher, Al and Staab, Steffen and Menges, Achim and Wortmann, Thomas}, booktitle = {Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries}, doi = {10.1007/978-3-031-37189-9_31}, editor = {Turrin, Michela and Andriotis, Charalampos and Rafiee, Azarakhsh}, isbn = {978-3-031-37189-9}, pages = {470--484}, publisher = {Springer Nature Switzerland}, title = {Building Information Validation and Reasoning Using Semantic Web Technologies}, url = {https://doi.org/10.1007/978-3-031-37189-9_31}, year = 2023 }
7. Elshani, D., Lombardi, A., Hernández, D., Staab, S., Fisher, A., & Wortmann, T. (2023). BHoM to bhOWL converter. DaRUS. https://doi.org/10.18419/darus-3364
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  @dataset{darus-3364_2023, author = {Elshani, Diellza and Lombardi, Alessio and Hernández, Daniel and Staab, Steffen and Fisher, Al and Wortmann, Thomas}, doi = {10.18419/darus-3364}, publisher = {DaRUS}, title = {BHoM to bhOWL converter}, url = {https://doi.org/10.18419/darus-3364}, version = {V1}, year = 2023 }
8. Galárraga, L., Hernández, D., Katim, A., & Hose, K. (2023). Visualizing How-Provenance Explanations for SPARQL Queries. In Y. Ding, J. Tang, J. F. Sequeda, L. Aroyo, C. Castillo, & G.-J. Houben (Eds.), Companion Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023 (pp. 212–216). ACM. https://doi.org/10.1145/3543873.3587350
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  @inproceedings{DBLP:conf/www/Galarraga0KH23, abstract = {Knowledge graphs (KGs) are vast collections of machine-readable information, usually modeled in RDF and queried with SPARQL. KGs have opened the door to a plethora of applications such as Web search or smart assistants that query and process the knowledge contained in those KGs. An important, but often disregarded, aspect of querying KGs is query provenance: explanations of the data sources and transformations that made a query result possible. In this article we demonstrate, through a Web application, the capabilities of SPARQLprov, an engine-agnostic method that annotates query results with how-provenance annotations. To this end, SPARQLprov resorts to query rewriting techniques, which make it applicable to already deployed SPARQL endpoints. We describe the principles behind SPARQLprov and discuss perspectives on visualizing how-provenance explanations for SPARQL queries. }, author = {Galárraga, Luis and Hernández, Daniel and Katim, Anas and Hose, Katja}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Companion Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023}, doi = {10.1145/3543873.3587350}, editor = {Ding, Ying and Tang, Jie and Sequeda, Juan F. and Aroyo, Lora and Castillo, Carlos and Houben, Geert-Jan}, eventdate = {30 April 2023 - 4 May 2023}, eventtitle = {ACM Web Conference 2023, WWW 2023}, language = {English}, pages = {212-216}, publisher = {ACM}, title = {Visualizing How-Provenance Explanations for SPARQL Queries}, url = {https://doi.org/10.1145/3543873.3587350}, venue = {Austin, Texas, USA}, year = 2023 }
9. Gregucci, C. (2023). Query Answering over the Polymorphic Web of Data. In C. Pesquita, H. Skaf-Molli, V. Efthymiou, S. Kirrane, A. Ngonga, D. Collarana, R. Cerqueira, M. Alam, C. Trojahn, & S. Hertling (Eds.), The Semantic Web: ESWC 2023 Satellite Events - Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings (Vol. 13998, pp. 255--265). Springer. https://doi.org/10.1007/978-3-031-43458-7_44
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  BibTeX
  @inproceedings{DBLP:conf/esws/Gregucci23, author = {Gregucci, Cosimo}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {The Semantic Web: {ESWC} 2023 Satellite Events - Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings}, doi = {10.1007/978-3-031-43458-7_44}, editor = {Pesquita, Catia and Skaf{-}Molli, Hala and Efthymiou, Vasilis and Kirrane, Sabrina and Ngonga, Axel and Collarana, Diego and Cerqueira, Renato and Alam, Mehwish and Trojahn, C{\'{a}}ssia and Hertling, Sven}, pages = {255--265}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Query Answering over the Polymorphic Web of Data}, url = {https://doi.org/10.1007/978-3-031-43458-7_44}, volume = 13998, year = 2023 }
10. Gregucci, C., Nayyeri, M., Hernández, D., & Staab, S. (2023). Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models. In Y. Ding, J. Tang, J. F. Sequeda, L. Aroyo, C. Castillo, & G.-J. Houben (Eds.), Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023 (pp. 2600–2610). ACM. https://doi.org/10.1145/3543507.3583358
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  BibTeX
  @inproceedings{gregucci2023link, abstract = {Predicting missing links between entities in a knowledge graph is a fundamental task to deal with the incompleteness of data on the Web. Knowledge graph embeddings map nodes into a vector space to predict new links, scoring them according to geometric criteria. Relations in the graph may follow patterns that can be learned, e.g., some relations might be symmetric and others might be hierarchical. However, the learning capability of different embedding models varies for each pattern and, so far, no single model can learn all patterns equally well. In this paper, we combine the query representations from several models in a unified one to incorporate patterns that are independently captured by each model. Our combination uses attention to select the most suitable model to answer each query. The models are also mapped onto a non-Euclidean manifold, the Poincaré ball, to capture structural patterns, such as hierarchies, besides relational patterns, such as symmetry. We prove that our combination provides a higher expressiveness and inference power than each model on its own. As a result, the combined model can learn relational and structural patterns. We conduct extensive experimental analysis with various link prediction benchmarks showing that the combined model outperforms individual models, including state-of-the-art approaches.}, archiveprefix = {arXiv}, author = {Gregucci, Cosimo and Nayyeri, Mojtaba and Hernández, Daniel and Staab, Steffen}, booktitle = {Proceedings of the {ACM} Web Conference 2023, {WWW} 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023}, doi = {10.1145/3543507.3583358}, editor = {Ding, Ying and Tang, Jie and Sequeda, Juan F. and Aroyo, Lora and Castillo, Carlos and Houben, Geert-Jan}, eprint = {2302.06229}, eventdate = {30 April 2023 - 4 May 2023}, eventtitle = {ACM Web Conference 2023}, language = {English}, pages = {2600-2610}, preprinturl = {https://doi.org/10.48550/arXiv.2302.06229}, primaryclass = {cs.AI}, publisher = {ACM}, title = {Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models}, url = {https://doi.org/10.1145/3543507.3583358}, venue = {Austin, Texas, USA}, year = 2023 }
11. He, Y., Nayyeri, M., Xiong, B., Zhu, Y., Kharlamov, E., & Staab, S. (2023). Can Pattern Learning Enhance Complex Logical Query Answering? CEUR Workshop Proceedings. https://hozo.jp/ISWC2023_PD-Industry-proc/ISWC2023_paper_463.pdf
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  BibTeX
  @inproceedings{he2023pattern, abstract = {Logical patterns, such as symmetry and composition, have been proven to be beneficial in the knowledge graph completion task. However, their influence has been unexplored in first-order logical (FOL) query reasoning methods. In this work, we present an inductive bias for query embedding models, Patternaware Cone Embedding (PConE), to support learning and reasoning with logical patterns. PConE combines the advantages of cones and the rotation operator for powerful algebraic operations for pattern inference. Our experiments demonstrate how the capability to capture logical patterns positively impacts the results of query answering.}, author = {He, Yunjie and Nayyeri, Mojtaba and Xiong, Bo and Zhu, Yuqicheng and Kharlamov, Evgeny and Staab, Steffen}, booktitle = {CEUR Workshop Proceedings}, title = {Can Pattern Learning Enhance Complex Logical Query Answering?}, url = {https://hozo.jp/ISWC2023_PD-Industry-proc/ISWC2023_paper_463.pdf}, venue = {Thhe 22nd International Semantic Web Conference (ISWC 2023 posters and demos)}, year = 2023 }
12. Hosseini, A. S., & Staab, S. (2023). Emotional Framing in the Spreading of False and True Claims. Proceedings of the 15th ACM Web Science Conference 2023, 96--106.
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  BibTeX
  @inproceedings{hosseini2023emotional, author = {Hosseini, Akram Sadat and Staab, Steffen}, booktitle = {Proceedings of the 15th ACM Web Science Conference 2023}, pages = {96--106}, title = {Emotional Framing in the Spreading of False and True Claims}, year = 2023 }
13. Lu, J., Shen, J., Xiong, B., Ma, W., Staab, S., & Yang, C. (2023). HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting. The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023. https://arxiv.org/abs/2304.05973
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  BibTeX
  @conference{lu2023hiprompt, author = {Lu, Jiaying and Shen, Jiaming and Xiong, Bo and Ma, Wenjing and Staab, Steffen and Yang, Carl}, booktitle = {The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023}, title = {HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting}, url = {https://arxiv.org/abs/2304.05973}, year = 2023 }
14. Minh Ho Nguyen, D., Ngoc Pham, T., Tuong Diep, N., Phan, N., Pham, Q., Tong, V., T. Nguyen, B., Hoang Le, N., Ho, N., Xie, P., Sonntag, D., & Niepert, M. (2023). On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation. Advances in Neural Information Processing Systems (NeurIPS), Workshop on Robustness of Zero/Few-Shot Learning in Foundation Models. https://arxiv.org/pdf/2311.11096.pdf
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  BibTeX
  @conference{minhhonguyen2023distribution, author = {Minh Ho Nguyen, Duy and Ngoc Pham, Tan and Tuong Diep, Nghiem and Phan, Nghi and Pham, Quang and Tong, Vinh and T. Nguyen, Binh and Hoang Le, Ngan and Ho, Nhat and Xie, Pengtao and Sonntag, Daniel and Niepert, Mathias}, booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Workshop on robustness of zero/few-shot learning in foundation models}, title = {On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation}, url = {https://arxiv.org/pdf/2311.11096.pdf}, year = 2023 }
15. Minh Ho Nguyen, D., Nguyen, H., N. T. Mai, T., Tri, C., T. Nguyen, B., Ho, N., Swoboda, P., Albarqouni, S., Xie, P., & Sonntag, D. (2023). Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering. Proceedings of the AAAI Conference on Artificial Intelligence. https://ojs.aaai.org/index.php/AAAI/article/view/26687
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  BibTeX
  @conference{mhnguyen2023joint, author = {Minh Ho Nguyen, Duy and Nguyen, Hoang and N. T. Mai, Truong and Tri, Cao and T. Nguyen, Binh and Ho, Nhat and Swoboda, Paul and Albarqouni, Shadi and Xie, Pengtao and Sonntag, Daniel}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, title = {Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/26687}, year = 2023 }
16. Minh Ho Nguyen, D., Nguyen, H., T Diep, N., N Pham, T., Cao, T., T Nguyen, B., Swoboda, P., Ho, N., Albarqouni, S., Xie, P., Sonntag, D., & Niepert, M. (2023). LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching. Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023). https://proceedings.neurips.cc/paper_files/paper/2023/file/58cc11cda2a2679e8af5c6317aed0af8-Paper-Conference.pdf
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  BibTeX
  @conference{mhnguyen2023lvmmed, author = {Minh Ho Nguyen, Duy and Nguyen, Hoang and T Diep, Nghiem and N Pham, Tan and Cao, Tri and T Nguyen, Binh and Swoboda, Paul and Ho, Nhat and Albarqouni, Shadi and Xie, Pengtao and Sonntag, Daniel and Niepert, Mathias}, booktitle = {Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023)}, title = {LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching}, url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/58cc11cda2a2679e8af5c6317aed0af8-Paper-Conference.pdf}, year = 2023 }
17. Mjalled, A., Torres, E., & Mönnigmann, M. (2023). Reduced-order modeling framework using two-level neural networks. PAMM, n/a(n/a), Article n/a. https://doi.org/10.1002/pamm.202300061
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  BibTeX
  @article{https://doi.org/10.1002/pamm.202300061, abstract = {Abstract Established reduced-order modeling (ROM) methods, for instance, Galerkin-projection, approximate the solution by linearly projecting high-dimensional spaces to a lower-dimensional space spanned by the reduced basis. However, the accuracy of these methods may be insufficient for complex and multiscale simulations due to the restriction to a linear space. Alternatively, autoencoders (AEs) can be used for nonlinear dimensionality reduction. We combine nonlinear dimensionality reduction techniques with time series prediction to build data-driven ROMs. The presented framework consists of two-level neural networks. The high-dimensional space is nonlinearly compressed in the first level using the encoder function of the AE. Subsequently, the temporal evolution of the latent vector is predicted in the second level. The original solution can be easily reconstructed using the decoder function of the AE. In comparison with the projection-based ROMs, for example, proper orthogonal decomposition (POD) with Galerkin projection, this framework allows naturally to include parameters in the prediction without nonlinear interpolation of the linear basis. We demonstrate the framework on a two-dimensional flow field simulation around circular bodies parameterized with the inlet fluid velocity.}, author = {Mjalled, Ali and Torres, Edgar and Mönnigmann, Martin}, doi = {https://doi.org/10.1002/pamm.202300061}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/pamm.202300061}, journal = {PAMM}, number = {n/a}, pages = {e202300061}, title = {Reduced-order modeling framework using two-level neural networks}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pamm.202300061}, volume = {n/a}, year = 2023 }
18. Monninger, T., Schmidt, J., Rupprecht, J., Raba, D., Jordan, J., Frank, D., Staab, S., & Dietmayer, K. (2023). SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks. IEEE Robotics and Automation Letters, 1–8. https://doi.org/10.1109/LRA.2023.3234771
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  BibTeX
  @article{10008009, abstract = {Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Network encoder and task-specific decoders. The heterogeneous graphs, whose structures are defined by an ontology, consist of different nodes with type-specific node features and different relations with type-specific edge features. In order to exploit all the information given by these graphs, we propose to use cascaded layers of graph convolution. The result is an encoding of the scene. Task-specific decoders can be applied to predict desired attributes of the scene. Extensive evaluation on two diverse binary node classification tasks show the main strength of this methodology: despite being generic, it even manages to outperform task-specific baselines. The further application of our methodology to the task of node classification in various knowledge graphs shows its transferability to other domains.}, author = {Monninger, Thomas and Schmidt, Julian and Rupprecht, Jan and Raba, David and Jordan, Julian and Frank, Daniel and Staab, Steffen and Dietmayer, Klaus}, doi = {10.1109/LRA.2023.3234771}, issn = {2377-3766}, journal = {IEEE Robotics and Automation Letters}, pages = {1-8}, title = {SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks}, year = 2023 }
19. Monninger, T., Weber, A., & Staab, S. (2023). Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data. 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022, Macau, China, October 8-12, 2022, 1583--1590. https://doi.org/10.1109/ITSC57777.2023.10422549
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  BibTeX
  @inproceedings{DBLP:conf/itsc/MonningerWS23, author = {Monninger, Thomas and Weber, Andreas and Staab, Steffen}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {25th {IEEE} International Conference on Intelligent Transportation Systems, {ITSC} 2022, Macau, China, October 8-12, 2022}, doi = {10.1109/ITSC57777.2023.10422549}, pages = {1583--1590}, publisher = {{IEEE}}, title = {Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data}, url = {https://doi.org/10.1109/ITSC57777.2023.10422549}, year = 2023 }
20. Morales-Alvarez, W., Certad, N., Roitberg, A., Stiefelhagen, R., & Olaverri-Monreal, C. (2023). On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars. 2023 IEEE International Conference on Intelligent Transportation Systems (ITSC).
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  BibTeX
  @article{morales2023transferability, author = {Morales-Alvarez, Walter and Certad, Novel and Roitberg, Alina and Stiefelhagen, Rainer and Olaverri-Monreal, Cristina}, booktitle = {2023 IEEE International Conference on Intelligent Transportation Systems (ITSC)}, organization = {IEEE}, title = {On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars}, year = 2023 }
21. Nayyeri, M., Wang, Z., Akter, Mst. M., Alam, M. M., Rony, M. R. A. H., Lehmann, J., & Staab, S. (2023). Integrating Knowledge Graph Embeddings and Pre-trained Language Models in Hypercomplex Spaces. In The Semantic Web – ISWC 2023. https://doi.org/10.1007/978-3-031-47240-4_21
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  BibTeX
  @incollection{nayyeri2023integrating, author = {Nayyeri, Mojtaba and Wang, Zihao and Akter, Mst. Mahfuja and Alam, Mirza Mohtashim and Rony, Md Rashad Al Hasan and Lehmann, Jens and Staab, Steffen}, doi = {10.1007/978-3-031-47240-4_21}, isbn = {9783031472398}, issn = {0302-9743}, journal = {The Semantic Web – ISWC 2023}, title = {Integrating Knowledge Graph Embeddings and Pre-trained Language Models in Hypercomplex Spaces}, url = {http://dx.doi.org/10.1007/978-3-031-47240-4_21}, year = 2023 }
22. Potyka, N., Zhu, Y., He, Y., Kharlamov, E., & Staab, S. (2023). Robust Knowledge Extraction from Large Language Models using Social Choice Theory.
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  BibTeX
  @conference{noauthororeditor2023robust, abstract = {Large-language models (LLMs) have the potential to support a wide range of applications like conversational agents, creative writing, text improvement, and general query answering. However, they are ill-suited for query answering in high-stake domains like medicine because they generate answers at random and their answers are typically not robust - even the same query can result in different answers when prompted multiple times. In order to improve the robustness of LLM queries, we propose using ranking queries repeatedly and to aggregate the queries using methods from social choice theory. We study ranking queries in diagnostic settings like medical and fault diagnosis and discuss how the Partial Borda Choice function from the literature can be applied to merge multiple query results. We discuss some additional interesting properties in our setting and evaluate the robustness of our approach empirically.}, author = {Potyka, Nico and Zhu, Yuqicheng and He, Yunjie and Kharlamov, Evgeny and Staab, Steffen}, title = {Robust Knowledge Extraction from Large Language Models using Social Choice Theory}, year = 2023 }
23. Qian, C., Manolache, A., Ahmed, K., Zeng, Z., den Broeck, G. V., Niepert, M., & Morris, C. (2023). Probabilistic Task-Adaptive Graph Rewiring. ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators. https://openreview.net/forum?id=YsHKrMPHE1
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  BibTeX
  @inproceedings{qian2023probabilistic, author = {Qian, Chendi and Manolache, Andrei and Ahmed, Kareem and Zeng, Zhe and den Broeck, Guy Van and Niepert, Mathias and Morris, Christopher}, booktitle = {ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators}, title = {Probabilistic Task-Adaptive Graph Rewiring}, url = {https://openreview.net/forum?id=YsHKrMPHE1}, year = 2023 }

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For further information about our Research Topics, please visit:

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