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Here you can find all the publications from the Institute for Artificial Intelligence.

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Institute Publications

2025
1. Demartini, G., Hose, K., Acosta, M., Palmonari, M., Cheng, G., Skaf-Molli, H., Ferranti, N., Hernández, D., & Hogan, A. (2025). The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part III (Vol. 15233). Springer. https://doi.org/10.1007/978-3-031-77847-6
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  @proceedings{DBLP:conf/semweb/2024-3, bibsource = {dblp computer science bibliography, https://dblp.org}, doi = {10.1007/978-3-031-77847-6}, editor = {Demartini, Gianluca and Hose, Katja and Acosta, Maribel and Palmonari, Matteo and Cheng, Gong and Skaf{-}Molli, Hala and Ferranti, Nicolas and Hern{\'{a}}ndez, Daniel and Hogan, Aidan}, isbn = {978-3-031-77846-9}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part III}, url = {https://doi.org/10.1007/978-3-031-77847-6}, volume = 15233, year = 2025 }
2. Demartini, G., Hose, K., Acosta, M., Palmonari, M., Cheng, G., Skaf-Molli, H., Ferranti, N., Hernández, D., & Hogan, A. (2025). The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part I (Vol. 15231). Springer. https://doi.org/10.1007/978-3-031-77844-5
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  @proceedings{DBLP:conf/semweb/2024-1, bibsource = {dblp computer science bibliography, https://dblp.org}, doi = {10.1007/978-3-031-77844-5}, editor = {Demartini, Gianluca and Hose, Katja and Acosta, Maribel and Palmonari, Matteo and Cheng, Gong and Skaf{-}Molli, Hala and Ferranti, Nicolas and Hern{\'{a}}ndez, Daniel and Hogan, Aidan}, isbn = {978-3-031-77843-8}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part I}, url = {https://doi.org/10.1007/978-3-031-77844-5}, volume = 15231, year = 2025 }
3. Demartini, G., Hose, K., Acosta, M., Palmonari, M., Cheng, G., Skaf-Molli, H., Ferranti, N., Hernández, D., & Hogan, A. (2025). The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part II (Vol. 15232). Springer. https://doi.org/10.1007/978-3-031-77850-6
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  @proceedings{DBLP:conf/semweb/2024-2, bibsource = {dblp computer science bibliography, https://dblp.org}, doi = {10.1007/978-3-031-77850-6}, editor = {Demartini, Gianluca and Hose, Katja and Acosta, Maribel and Palmonari, Matteo and Cheng, Gong and Skaf{-}Molli, Hala and Ferranti, Nicolas and Hern{\'{a}}ndez, Daniel and Hogan, Aidan}, isbn = {978-3-031-77849-0}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part II}, url = {https://doi.org/10.1007/978-3-031-77850-6}, volume = 15232, year = 2025 }
4. Elhalawati, A., Dimou, A., Hartig, O., & Hernández, D. (2025). Flexible RML-Based Mapping of Property Graphs to RDF. In S. Dumbrava & R. Tommasini (eds.), Proceedings of the Workshops of the EDBT/ICDT 2025 Joint Conference (March 25-28, 2025), Barcelona, Spain. CEUR.
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  @inproceedings{elhalawati2025flexible, abstract = {RDF graphs and (Labeled) Property Graphs (PGs) have emerged as data models for representing graph databases. Given the differences between the two models, ensuring interoperability between them has become essential, to leverage the strengths of both models. Various approaches have been proposed to map PGs to RDF graphs. However, these approaches differ in terms of structure, representation, size of the generated RDF graph, and degree of configuration provided to the user, making direct comparisons challenging. While declarative methods prevailed to construct RDF graphs from other data formats, the mapping languages proposed for such transformations have not been considered so far for mapping PGs to RDF graphs. In this work, we provide a representation of PG-to-RDF approaches through templates described using RML, a mapping language to construct RDF graphs from heterogeneous data. We show that all considered PG-to-RDF approaches can be represented in RML and, by having a uniform representation of them, we can compare them showcasing their differences. Finally, we show that not only can RML be used to capture PG-to-RDF mappings, but it actually offers more expressive power than the considered PG-to-RDF approaches.}, author = {Elhalawati, Ali and Dimou, Anastasia and Hartig, Olaf and Hernández, Daniel}, booktitle = {Proceedings of the Workshops of the EDBT/ICDT 2025 Joint Conference (March 25-28, 2025), Barcelona, Spain}, editor = {Dumbrava, Stefania and Tommasini, Riccardo}, eventdate = {March 25, 2025}, eventtitle = {Transforming Graph Data Workshop}, language = {English}, publisher = {CEUR}, title = {Flexible RML-Based Mapping of Property Graphs to RDF}, venue = {Barcelona, Spain}, year = 2025 }
5. He, Y., Xiong, B., Hernández, D., Zhu, Y., Kharlamov, E., & Staab, S. (2025). DAGE: DAG Query Answering via Relational Combinator with Logical Constraints. The Web Conference 2025. https://openreview.net/forum?id=x1nXBzUknn
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  @inproceedings{he2025dage, abstract = {Predicting answers to queries over knowledge graphs is called a complex reasoning task because answering a query requires subdividing it into subqueries. Existing query embedding methods use this decomposition to compute the embedding of a query as the combination of the embedding of the subqueries. This requirement limits the answerable queries to queries having a single free variable and being decomposable, which are called tree-form queries and correspond to the SROI^- description logic. In this paper, we define a more general set of queries, called DAG queries and formulated in the ALCOIR description logic, propose a query embedding method for them, called DAGE, and a new benchmark to evaluate query embeddings on them. Given the computational graph of a DAG query, DAGE combines the possibly multiple paths between two nodes into a single path with a trainable operator that represents the intersection of relations and learns DAG-DL from tautologies. We show that it is possible to implement DAGE on top of existing query embedding methods, and we empirically measure the improvement of our method over the results of vanilla methods evaluated in tree-form queries that approximate the DAG queries of our proposed benchmark.}, author = {He, Yunjie and Xiong, Bo and Hern{\'a}ndez, Daniel and Zhu, Yuqicheng and Kharlamov, Evgeny and Staab, Steffen}, booktitle = {THE WEB CONFERENCE 2025}, language = {en}, preprinturl = {https://doi.org/10.48550/arXiv.2410.22105}, title = {DAGE: DAG Query Answering via Relational Combinator with Logical Constraints}, url = {https://openreview.net/forum?id=x1nXBzUknn}, year = 2025 }
2024
1. Pan, X., Hernández, D., Seifer, P., Lämmel, R., & Staab, S. (2024). eSPARQL: Representing and Reconciling Agnostic and Atheistic Beliefs in RDF-star Knowledge Graphs. In G. Demartini, K. Hose, M. Acosta, M. Palmonari, G. Cheng, H. Skaf-Molli, N. Ferranti, D. Hernández, & A. Hogan (eds.), The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part II (Vol. 15232, pp. 155–172). Springer. https://doi.org/10.1007/978-3-031-77850-6_9
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  @inproceedings{pan2024esparqlrepresentingreconcilingagnostic, abstract = {Over the past few years, we have seen the emergence of large knowledge graphs combining information from multiple sources. Sometimes, this information is provided in the form of assertions about other assertions, defining contexts where assertions are valid. A recent extension to RDF which admits statements over statements, called RDF-star, is in revision to become a W3C standard. However, there is no proposal for a semantics of these RDF-star statements nor a built-in facility to operate over them. In this paper, we propose a query language for epistemic RDF-star metadata based on a four-valued logic, called eSPARQL. Our proposed query language extends SPARQL-star, the query language for RDF-star, with a new type of FROM clause to facilitate operating with multiple and sometimes conflicting beliefs. We show that the proposed query language can express four use case queries, including the following features: (i) querying the belief of an individual, (ii) the aggregating of beliefs, (iii) querying who is conflicting with somebody, and (iv) beliefs about beliefs (i.e., nesting of beliefs).}, archiveprefix = {arXiv}, author = {Pan, Xinyi and Hernández, Daniel and Seifer, Philipp and Lämmel, Ralf and Staab, Steffen}, booktitle = {The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part II}, doi = {10.1007/978-3-031-77850-6_9}, editor = {Demartini, Gianluca and Hose, Katja and Acosta, Maribel and Palmonari, Matteo and Cheng, Gong and Skaf-Molli, Hala and Ferranti, Nicolas and Hernández, Daniel and Hogan, Aidan}, eprint = {2407.21483}, eventtitle = {23rd International Semantic Web Conference}, language = {English}, month = {11}, pages = {155-172}, preprinturl = {https://arxiv.org/abs/2407.21483}, primaryclass = {cs.AI}, publisher = {Springer}, title = {eSPARQL: Representing and Reconciling Agnostic and Atheistic Beliefs in RDF-star Knowledge Graphs}, url = {https://doi.org/10.1007/978-3-031-77850-6_9}, venue = {Baltimore, MD, USA}, volume = 15232, year = 2024 }
2. He, Y., Hernandez, D., Nayyeri, M., Xiong, B., Zhu, Y., Kharlamov, E., & Staab, S. (2024). Generating SROI⁻ Ontologies via Knowledge Graph Query Embedding Learning. In U. Endriss, F. S. Melo, K. Bach, A. J. Bugarín Diz, J. M. Alonso-Moral, S. Barro, & F. Heintz (eds.), ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024) (Vol. 392, pp. 4279–4286). IOS Press. https://doi.org/10.3233/FAIA241002
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  @inproceedings{he2024generatingsroiontologiesknowledge, abstract = {Query embedding approaches answer complex logical queries over incomplete knowledge graphs (KGs) by computing and operating on low-dimensional vector representations of entities, relations, and queries. However, current query embedding models heavily rely on excessively parameterized neural networks and cannot explain the knowledge learned from the graph. We propose a novel query embedding method, AConE, which explains the knowledge learned from the graph in the form of SROI− description logic axioms while being more parameter-efficient than most existing approaches. AConE associates queries to SROI− description logic concepts. Every SROI− concept is embedded as a cone in complex vector space, and each SROI− relation is embedded as a transformation that rotates and scales cones. We show theoretically that AConE can learn SROI− axioms, and defines an algebra whose operations correspond one-to-one to SROI− description logic concept constructs. Our empirical study on multiple query datasets shows that AConE achieves superior results over previous baselines with fewer parameters. Notably on the WN18RR dataset, AConE achieves significant improvement over baseline models. We provide comprehensive analyses showing that the capability to represent axioms positively impacts the results of query answering.}, archiveprefix = {arXiv}, author = {He, Yunjie and Hernandez, Daniel and Nayyeri, Mojtaba and Xiong, Bo and Zhu, Yuqicheng and Kharlamov, Evgeny and Staab, Steffen}, booktitle = {ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024)}, doi = {10.3233/FAIA241002}, editor = {Endriss, Ulle and Melo, Francisco S. and Bach, Kerstin and Bugarín Diz, Alberto José and Alonso-Moral, Jose Maria and Barro, Senén and Heintz, Fredrik}, eprint = {2407.09212}, isbn = {978-1-64368-548-9}, language = {English}, month = {10}, pages = {4279 - 4286}, preprinturl = {https://arxiv.org/abs/2407.09212}, primaryclass = {cs.AI}, publisher = {IOS Press}, series = {Frontiers in Artificial Intelligence and Applications}, title = {Generating SROI⁻ Ontologies via Knowledge Graph Query Embedding Learning}, url = {https://doi.org/10.3233/FAIA241002}, venue = {Santiago de Compostela, Spain}, volume = 392, year = 2024 }
3. Navigli, R., Lo Pinto, M., Silvestri, P., Rotondi, D., Ciciliano, S., & Scirè, A. (2024). NounAtlas: Filling the Gap in Nominal Semantic Role Labeling. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 16245–16258). Association for Computational Linguistics. https://aclanthology.org/2024.acl-long.857
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  @inproceedings{navigli-etal-2024-nounatlas, abstract = {Despite significant advances in Semantic Role Labeling (SRL), much work in this field has been carried out with a focus on verbal predicates, with the research on nominal SRL lagging behind. In many contexts, however, nominal predicates are often as informative as verbal ones, thus needing proper treatment. In this paper we aim to fill this gap and make nominal SRL a first-class citizen. We introduce a novel approach to create the first large-scale, high-quality inventory of nominal predicates and organize them into semantically-coherent frames. Although automatically created, NounAtlas {--} our frame inventory {--} is subsequently fully validated. We then put forward a technique to generate silver training data for nominal SRL and show that a state-of-the-art SRL model can achieve good performance. Interestingly, thanks to our design choices which enable seamless integration of our predicate inventory with its verbal counterpart, we can mix verbal and nominal data and perform robust SRL on both types of predicates.}, address = {Bangkok, Thailand}, author = {Navigli, Roberto and Lo Pinto, Marco and Silvestri, Pasquale and Rotondi, Dennis and Ciciliano, Simone and Scir{\`e}, Alessandro}, booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, editor = {Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek}, month = {08}, pages = {16245--16258}, publisher = {Association for Computational Linguistics}, title = {NounAtlas: Filling the Gap in Nominal Semantic Role Labeling}, url = {https://aclanthology.org/2024.acl-long.857}, year = 2024 }
4. Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024). Inferring SHACL Constraints for Results of Composable Graph Queries (Extended Abstract). In L. Giordano, J. C. Jung, & A. Ozaki (Eds.), Proceedings of the 37th International Workshop on Description Logics (DL 2024), Bergen, Norway, June 18-21, 2024 (Vol. 3739). CEUR-WS.org. https://ceur-ws.org/Vol-3739/abstract-23.pdf
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  @inproceedings{DBLP:conf/dlog/Seifer0LS24, abstract = {SPARQL CONSTRUCT queries allow for the specification of data processing pipelines that transform given input graphs into new output graphs. Input graphs are now commonly constrained through SHACL shapes allowing for both their validation and aiding users (as well as tools) in understanding their structure. 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 itself. In our recent work, From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries, we studied the derivation of shape constraints that hold on all possible output graphs of a given SPARQL CONSTRUCT query by axiomatizing the query and the shapes with the ALCHOI description logic. This extended abstract summarizes our previous work.}, author = {Seifer, Philipp and Hern{\'{a}}ndez, Daniel and L{\"{a}}mmel, Ralf and Staab, Steffen}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 37th International Workshop on Description Logics (DL 2024), Bergen, Norway, June 18-21, 2024}, editor = {Giordano, Laura and Jung, Jean Christoph and Ozaki, Ana}, month = {06}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Inferring {SHACL} Constraints for Results of Composable Graph Queries (Extended Abstract)}, url = {https://ceur-ws.org/Vol-3739/abstract-23.pdf}, volume = 3739, year = 2024 }
5. 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. 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 }
6. Blomqvist, E., García-Castro, R., Hernández, D., Hitzler, P., Lindecrantz, M., & Poveda-Villalón, M. (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 }
7. 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 }
8. 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 }
9. 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 }
10. 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 }
11. 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 }
12. 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 }
13. 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. 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 }
14. 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 }
15. 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. https://doi.org/10.1145/3589334.3645557
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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 }
16. “Hosseini, A. S., & “Staab, S. (2024). Disambiguating Emotional Connotations of Words Using Contextualized Word Representations.
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  @inproceedings{hosseini2024disambiguating, author = {"Hosseini, Akram Sadat" and "Staab, Steffen"}, title = {Disambiguating Emotional Connotations of Words Using Contextualized Word Representations}, year = 2024 }
17. 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 }
18. Chamon, L. F. O., Karimi, M. R., & Korba, A. (2024). Constrained Sampling with Primal-Dual Langevin Monte Carlo. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). https://doi.org/10.48550/arXiv.2411.00568
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  @inproceedings{chamon2025constrainedsamplingprimalduallangevin, abstract = {This work considers the problem of sampling from a probability distribution known up to a normalization constant while satisfying a set of statistical constraints specified by the expected values of general nonlinear functions. This problem finds applications in, e.g., Bayesian inference, where it can constrain moments to evaluate counterfactual scenarios or enforce desiderata such as prediction fairness. Methods developed to handle support constraints, such as those based on mirror maps, barriers, and penalties, are not suited for this task. This work therefore relies on gradient descent-ascent dynamics in Wasserstein space to put forward a discrete-time primal-dual Langevin Monte Carlo algorithm (PD-LMC) that simultaneously constrains the target distribution and samples from it. We analyze the convergence of PD-LMC under standard assumptions on the target distribution and constraints, namely (strong) convexity and log-Sobolev inequalities. To do so, we bring classical optimization arguments for saddle-point algorithms to the geometry of Wasserstein space. We illustrate the relevance and effectiveness of PD-LMC in several applications.}, archiveprefix = {arXiv}, author = {Chamon, Luiz F. O. and Karimi, Mohammad Reza and Korba, Anna}, booktitle = {In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024)}, doi = {https://doi.org/10.48550/arXiv.2411.00568}, eprint = {2411.00568}, eventtitle = {2024 Conference on Neural Information Processing Systems}, language = {en}, primaryclass = {stat.ML}, title = {Constrained Sampling with Primal-Dual Langevin Monte Carlo}, url = {https://arxiv.org/abs/2411.00568}, year = 2024 }
19. Crum, E., Santis, A. D., Ovide, M., Pan, J., Pisu, A., Lazzari, N., & Rudolph, S. (2024). Enriching Ontologies with Disjointness Axioms using Large Language Models. International Semantic Web Conference 2024. https://doi.org/10.48550/arXiv.2410.03235
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  @inproceedings{crum2024enrichingontologiesdisjointnessaxioms, archiveprefix = {arXiv}, author = {Crum, Elias and Santis, Antonio De and Ovide, Manon and Pan, Jiaxin and Pisu, Alessia and Lazzari, Nicolas and Rudolph, Sebastian}, booktitle = {Workshop on Knowledge Base Construction from Pre-Trained Language Models}, doi = {https://doi.org/10.48550/arXiv.2410.03235}, eprint = {2410.03235}, journal = {International Semantic Web Conference 2024}, language = {en}, primaryclass = {cs.AI}, title = {Enriching Ontologies with Disjointness Axioms using Large Language Models}, url = {https://arxiv.org/abs/2410.03235}, year = 2024 }
20. Das, A., Fathallah, N., & Obretincheva, N. (2024). Navigating Nulls, Numbers and Numerous Entities: Robust Knowledge Base Construction from Large Language Models. In Knowledge Base Construction from Pre-trained Language Models Challenge Workshop, ISWC′24.
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  @preprint{das2024navigating, author = {Das, Arunav and Fathallah, Nadeen and Obretincheva, Nicole}, booktitle = {Knowledge Base Construction from Pre-trained Language Models Challenge Workshop, ISWC'24.}, title = {Navigating Nulls, Numbers and Numerous Entities: Robust Knowledge Base Construction from Large Language Models.}, year = 2024 }
21. Diaz Ochoa, J. G., Mustafa, F. E., Weil, F., Wang, Y., Kama, K., & Knott, M. (2024). The aluminum standard: using generative Artificial Intelligence tools to synthesize and annotate non-structured patient data. BMC Medical Informatics and Decision Making, 24, Article 1. https://doi.org/10.1186/s12911-024-02825-4
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  @article{diazochoa2024aluminum, abstract = {Medical narratives are fundamental to the correct identification of a patient’s health condition. This is not only because it describes the patient’s situation. It also contains relevant information about the patient’s context and health state evolution. Narratives are usually vague and cannot be categorized easily. On the other hand, once the patient’s situation is correctly identified based on a narrative, it is then possible to map the patient’s situation into precise classification schemas and ontologies that are machine-readable. To this end, language models can be trained to read and extract elements from these narratives. However, the main problem is the lack of data for model identification and model training in languages other than English. First, gold standard annotations are usually not available due to the high level of data protection for patient data. Second, gold standard annotations (if available) are difficult to access. Alternative available data, like MIMIC (Sci Data 3:1, 2016) is written in English and for specific patient conditions like intensive care. Thus, when model training is required for other types of patients, like oncology (and not intensive care), this could lead to bias. To facilitate clinical narrative model training, a method for creating high-quality synthetic narratives is needed.}, author = {Diaz Ochoa, Juan G. and Mustafa, Faizan E. and Weil, Felix and Wang, Yi and Kama, Kudret and Knott, Markus}, doi = {10.1186/s12911-024-02825-4}, issn = {14726947}, journal = {BMC Medical Informatics and Decision Making}, number = 1, pages = {409--}, refid = {Diaz Ochoa2024}, title = {The aluminum standard: using generative Artificial Intelligence tools to synthesize and annotate non-structured patient data}, url = {https://doi.org/10.1186/s12911-024-02825-4}, volume = 24, year = 2024 }
22. 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 }
23. Ding, Z., Wu, J., Wu, J., Xia, Y., Xiong, B., & Tresp, V. (2024). Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024 (pp. 355–373). Association for Computational Linguistics. https://aclanthology.org/2024.findings-emnlp.20
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  @inproceedings{DBLP:conf/emnlp/DingWW0XT24, author = {Ding, Zifeng and Wu, Jingcheng and Wu, Jingpei and Xia, Yan and Xiong, Bo and Tresp, Volker}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Findings of the Association for Computational Linguistics: {EMNLP} 2024, Miami, Florida, USA, November 12-16, 2024}, editor = {Al{-}Onaizan, Yaser and Bansal, Mohit and Chen, Yun{-}Nung}, pages = {355--373}, publisher = {Association for Computational Linguistics}, title = {Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs}, url = {https://aclanthology.org/2024.findings-emnlp.20}, year = 2024 }
24. Fathallah, N., Bhole, M., & Staab, S. (2024). Empowering the Deaf and Hard of Hearing Community: Improving Video Captions with Large Language Models. In In Proceedings of the 11th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion.
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  @preprint{fathallah2024empowering, author = {Fathallah, Nadeen and Bhole, Monika and Staab, Steffen}, booktitle = {In Proceedings of the 11th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion. }, title = {Empowering the Deaf and Hard of Hearing Community: Improving Video Captions with Large Language Models}, year = 2024 }
25. Fathallah, N., Das, A., De Giorgis, G., Poltronieri, A., Haase, P., & Kovriguina, L. (2024). NeOn-GPT: A Large Language Model-Powered Pipeline for Ontology Learning. Special Track on Large Language Models for Knowledge Engineering, Extended Semantic Web Conference, 2024. (ESWC 2024). https://doi.org/10.5281/ZENODO.11221930
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  @inproceedings{https://doi.org/10.5281/zenodo.11221930, author = {Fathallah, Nadeen and Das, Arunav and De Giorgis, Giorgis and Poltronieri, Andrea and Haase, Peter and Kovriguina, Liubov}, booktitle = {Special Track on Large Language Models for Knowledge Engineering, Extended Semantic Web Conference, 2024. (ESWC 2024)}, copyright = {Creative Commons Attribution 4.0 International}, doi = {10.5281/ZENODO.11221930}, language = {en}, title = {NeOn-GPT: A Large Language Model-Powered Pipeline for Ontology Learning.}, url = {https://zenodo.org/doi/10.5281/zenodo.11221930}, year = 2024 }
26. Fathallah, N., Staab, S., & Algergawy, A. (2024). LLMs4Life: Large language models for ontology learning in life sciences. In In Proceedings of the ELMKE Workshop on Evaluation of Language Models in Knowledge Engineering co-located with EKAW-24 (24th International Conference on Knowledge Engineering and Knowledge Management).
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  @preprint{fathallah2024llms4life, author = {Fathallah, Nadeen and Staab, Steffen and Algergawy, Alsayed}, booktitle = {In Proceedings of the ELMKE Workshop on Evaluation of Language Models in Knowledge Engineering co-located with EKAW-24 (24th International Conference on Knowledge Engineering and Knowledge Management).}, title = {LLMs4Life: Large language models for ontology learning in life sciences.}, year = 2024 }
27. 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 }
28. Hedeshy, R., Menges, R., & Staab, S. (2024). Raw audio samples of the CNVVE dataset [DaRUS]. https://doi.org/10.18419/DARUS-3897
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  @dataset{https://doi.org/10.18419/darus-3897, abstract = {This CNVVE Dataset contains raw audio samples encompassing six distinct classes of voice expressions, namely “Uh-huh” or “mm-hmm”, “Uh-uh” or“mm-mm”, “Hush” or “Shh”, “Psst”, “Ahem”, and Continuous humming, e.g., “hmmm.” Audio samples of each class are found in the respective folders. The samples are recorded through a dedicated website for data collection that defines the purpose and type of voice data by providing example recordings to participants as well as the expressions’ written equivalent, e.g., “Uh-huh”. Audio recordings were automatically saved in the .wav format and kept anonymous, with a sampling rate of 48 kHz and a bit depth of 32 bits.This dataset contains a raw version of the samples. A cleaned version of these samples can be found on https://doi.org/10.18419/darus-3898. For more info, please check the paper or feel free to contact the authors for any inquiries.}, author = {Hedeshy, Ramin and Menges, Raphael and Staab, Steffen}, doi = {10.18419/DARUS-3897}, publisher = {DaRUS}, title = {Raw audio samples of the CNVVE dataset}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3897}, year = 2024 }
29. Hedeshy, R., Menges, R., & Staab, S. (2024). CNVVE Dataset clean audio samples [DaRUS]. https://doi.org/10.18419/DARUS-3898
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  @dataset{https://doi.org/10.18419/darus-3898, abstract = {This CNVVE Dataset contains clean audio samples encompassing six distinct classes of voice expressions, namely “Uh-huh” or “mm-hmm”, “Uh-uh” or“mm-mm”, “Hush” or “Shh”, “Psst”, “Ahem”, and Continuous humming, e.g., “hmmm.” Audio samples of each class are found in the respective folders. These audio samples have undergone a thorough cleaning process. The raw samples are published in https://doi.org/10.18419/darus-3897. Initially, we applied the Google WebRTC voice activity detection (VAD) algorithm on the given audio files to remove noise or silence from the collected voice signals. The intensity was set to "2", which could be a value between "1" and "3". However, because of variations in the data, some files required additional manual cleaning. These outliers, characterized by sharp click sounds (such as those occurring at the end of recordings), were addressed. The samples are recorded through a dedicated website for data collection that defines the purpose and type of voice data by providing example recordings toparticipants as well as the expressions’ written equivalent, e.g., “Uh-huh”. Audio recordings were automatically saved in the .wav format and keptanonymous, with a sampling rate of 48 kHz and a bit depth of 32 bits. For more info, please check the paper or feel free to contact the authors for any inquiries.}, author = {Hedeshy, Ramin and Menges, Raphael and Staab, Steffen}, doi = {10.18419/DARUS-3898}, publisher = {DaRUS}, title = {CNVVE Dataset clean audio samples}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3898}, year = 2024 }
30. Jalali Farahani, F., Hanke, S., Dima, C., Heiberger, R. H., & Staab, S. (2024). Who is targeted? Detecting social group mentions in online political discussions. Companion Publication of the 16th ACM Web Science Conference, 24–25. https://doi.org/10.1145/3630744.3658412
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  @inproceedings{10.1145/3630744.3658412, abstract = {Social groups are central to political discussions. However, detecting social groups in text often relies on pre-determined socio-demographic categories or supervised learning methods that require extensive hand-labeled datasets. In this paper, we propose a methodology designed to leverage the potential of Large Language Models (LLMs) for the identification and annotation of social groups in text. The experiments show that open LLMs like Llama-2-70B-Chat and Mixtral-8-7B can reliably be used to annotate social groups in a few-shot scenario without the need for supervised learning. The automatically obtained annotations largely match human annotations on random samples from the Reddit Politosphere, resulting in micro-F1 scores of 0.71 and 0.83, respectively.}, address = {New York, NY, USA}, author = {Jalali Farahani, Farane and Hanke, Sara and Dima, Corina and Heiberger, Raphael Heiko and Staab, Steffen}, booktitle = {Companion Publication of the 16th ACM Web Science Conference}, doi = {10.1145/3630744.3658412}, isbn = {9798400704536}, location = {Stuttgart, Germany}, numpages = {2}, pages = {24–25}, publisher = {Association for Computing Machinery}, series = {Websci Companion '24}, title = {Who is targeted? Detecting social group mentions in online political discussions}, url = {https://doi.org/10.1145/3630744.3658412}, year = 2024 }
31. Liu, X., Liu, A., den Broeck, G. V., & Liang, Y. (2024). A Tractable Inference Perspective of Offline RL. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). https://doi.org/10.48550/arXiv.2311.00094
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  @inproceedings{liu2024tractableinferenceperspectiveoffline, abstract = {A popular paradigm for offline Reinforcement Learning (RL) tasks is to first fit the offline trajectories to a sequence model, and then prompt the model for actions that lead to high expected return. In addition to obtaining accurate sequence models, this paper highlights that tractability, the ability to exactly and efficiently answer various probabilistic queries, plays an important role in offline RL. Specifically, due to the fundamental stochasticity from the offline data-collection policies and the environment dynamics, highly non-trivial conditional/constrained generation is required to elicit rewarding actions. it is still possible to approximate such queries, we observe that such crude estimates significantly undermine the benefits brought by expressive sequence models. To overcome this problem, this paper proposes Trifle (Tractable Inference for Offline RL), which leverages modern Tractable Probabilistic Models (TPMs) to bridge the gap between good sequence models and high expected returns at evaluation time. Empirically, Trifle achieves the most state-of-the-art scores in 9 Gym-MuJoCo benchmarks against strong baselines. Further, owing to its tractability, Trifle significantly outperforms prior approaches in stochastic environments and safe RL tasks (e.g. with action constraints) with minimum algorithmic modifications.}, archiveprefix = {arXiv}, author = {Liu, Xuejie and Liu, Anji and den Broeck, Guy Van and Liang, Yitao}, booktitle = {In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024)}, doi = {https://doi.org/10.48550/arXiv.2311.00094}, eprint = {2311.00094}, eventtitle = {2024 Conference on Neural Information Processing Systems}, language = {en}, primaryclass = {cs.LG}, title = {A Tractable Inference Perspective of Offline RL}, url = {https://arxiv.org/abs/2311.00094}, year = 2024 }
32. Manolache, A., Tantaru, D., & Niepert, M. (2024). MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning. https://doi.org/10.48550/arXiv.2410.07981
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  @misc{manolache2024molmixsimpleeffectivebaseline, abstract = {In this work, we propose a simple transformer-based baseline for multimodal molecular representation learning, integrating three distinct modalities: SMILES strings, 2D graph representations, and 3D conformers of molecules. A key aspect of our approach is the aggregation of 3D conformers, allowing the model to account for the fact that molecules can adopt multiple conformations-an important factor for accurate molecular representation. The tokens for each modality are extracted using modality-specific encoders: a transformer for SMILES strings, a message-passing neural network for 2D graphs, and an equivariant neural network for 3D conformers. The flexibility and modularity of this framework enable easy adaptation and replacement of these encoders, making the model highly versatile for different molecular tasks. The extracted tokens are then combined into a unified multimodal sequence, which is processed by a downstream transformer for prediction tasks. To efficiently scale our model for large multimodal datasets, we utilize Flash Attention 2 and bfloat16 precision. Despite its simplicity, our approach achieves state-of-the-art results across multiple datasets, demonstrating its effectiveness as a strong baseline for multimodal molecular representation learning.}, archiveprefix = {arXiv}, author = {Manolache, Andrei and Tantaru, Dragos and Niepert, Mathias}, doi = {https://doi.org/10.48550/arXiv.2410.07981}, eprint = {2410.07981}, language = {en}, primaryclass = {cs.LG}, title = {MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning}, url = {https://arxiv.org/abs/2410.07981}, workshoptitle = {Machine Learning for Structural Biology}, year = 2024 }
33. Mougan, C., Ruggieri, S., State, L., Ferrara, A., & Staab, S. (2024). Measuring the Impact of Equal Treatment as Blindness via Explanations Disparity. https://openreview.net/forum?id=ndU9EvrVBH
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  @misc{mougan2024measuring, abstract = {Liberal political philosophy advocates for the policy of \emph{equal treatment as blindness}, which seeks to achieve fairness by treating individuals without considering their protected characteristics directly. However, this policy has faced longstanding criticism for perpetuating existing inequalities. In machine learning, this policy can be translated into the concept of \emph{fairness as unawareness}, and be measured using disparate treatment metrics such as Demographic Parity (a.k.a. Statistical Parity). Our analysis reveals that Demographic Parity does not faithfully measure whether individuals are being treated independently of the protected attribute by the model. We introduce the Explanation Disparity metric to measure fairness under \emph{equal treatment as blindness} policy. Our metric evaluates the fairness of predictive models by analyzing the extent to which the protected attribute can be inferred from the distribution of explanation values, specifically using Shapley values. The proposed metric tests for statistical independence of the explanation distributions over populations with different protected characteristics. We show the theoretical properties of "Explanation Disparity" and devise an equal treatment inspector based on the AUC of a Classifier Two-Sample Test. We experiment with synthetic and natural data to demonstrate and compare the notion with related ones. }, author = {Mougan, Carlos and Ruggieri, Salvatore and State, Laura and Ferrara, Antonio and Staab, Steffen}, language = {en}, title = {Measuring the Impact of Equal Treatment as Blindness via Explanations Disparity}, url = {https://openreview.net/forum?id=ndU9EvrVBH}, workshoptitle = {Algorithmic Fairness through the lens of Metrics and Evaluation}, year = 2024 }
34. Musekamp, D., Kalimuthu, M., Holzmüller, D., Takamoto, M., & Niepert, M. (2024). Active Learning for Neural PDE Solvers. NeurIPS 2024 Workshop on Data-Driven and Differentiable Simulations, Surrogates, and Solvers. https://openreview.net/forum?id=LD63WlGRQQ
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  @inproceedings{musekamp2024active, abstract = {Solving partial differential equations (PDEs) is a fundamental problem in engineering and science. While neural PDE solvers can be more efficient than established numerical solvers, they often require large amounts of training data that is costly to obtain. Active Learning (AL) could help surrogate models reach the same accuracy with smaller training sets by querying classical solvers with more informative initial conditions and PDE parameters. While AL is more common in other domains, it has yet to be studied extensively for neural PDE solvers. To bridge this gap, we introduce AL4PDE, a modular and extensible active learning benchmark. It provides multiple parametric PDEs and state-of-the-art surrogate models for the solver-in-the-loop setting, enabling the evaluation of existing and the development of new AL methods for PDE solving. We use the benchmark to evaluate batch active learning algorithms such as uncertainty- and feature-based methods. We show that AL reduces the average error by up to 71% compared to random sampling and significantly reduces worst-case errors. Moreover, AL generates similar datasets across repeated runs, with consistent distributions over the PDE parameters and initial conditions. The acquired datasets are reusable, providing benefits for surrogate models not involved in the data generation.}, author = {Musekamp, Daniel and Kalimuthu, Marimuthu and Holzm{\"u}ller, David and Takamoto, Makoto and Niepert, Mathias}, booktitle = {NeurIPS 2024 Workshop on Data-driven and Differentiable Simulations, Surrogates, and Solvers}, title = {Active Learning for Neural {PDE} Solvers}, url = {https://openreview.net/forum?id=LD63WlGRQQ}, year = 2024 }
35. 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 }
36. 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 }
37. 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 }
38. Peng, K., Wen, D., Yang, K., Luo, A., Chen, Y., Fu, J., Sarfraz, M. S., Roitberg, A., & Stiefelhagen, R. (2024). Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). https://doi.org/10.48550/arXiv.2409.17555
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  @inproceedings{peng2024advancingopensetdomaingeneralization, abstract = {In Open-Set Domain Generalization (OSDG), the model is exposed to both new variations of data appearance (domains) and open-set conditions, where both known and novel categories are present at test time. The challenges of this task arise from the dual need to generalize across diverse domains and accurately quantify category novelty, which is critical for applications in dynamic environments. Recently, meta-learning techniques have demonstrated superior results in OSDG, effectively orchestrating the meta-train and -test tasks by employing varied random categories and predefined domain partition strategies. These approaches prioritize a well-designed training schedule over traditional methods that focus primarily on data augmentation and the enhancement of discriminative feature learning. The prevailing meta-learning models in OSDG typically utilize a predefined sequential domain scheduler to structure data partitions. However, a crucial aspect that remains inadequately explored is the influence brought by strategies of domain schedulers during training. In this paper, we observe that an adaptive domain scheduler benefits more in OSDG compared with prefixed sequential and random domain schedulers. We propose the Evidential Bi-Level Hardest Domain Scheduler (EBiL-HaDS) to achieve an adaptive domain scheduler. This method strategically sequences domains by assessing their reliabilities in utilizing a follower network, trained with confidence scores learned in an evidential manner, regularized by max rebiasing discrepancy, and optimized in a bi-level manner. The results show that our method substantially improves OSDG performance and achieves more discriminative embeddings for both the seen and unseen categories.}, archiveprefix = {arXiv}, author = {Peng, Kunyu and Wen, Di and Yang, Kailun and Luo, Ao and Chen, Yufan and Fu, Jia and Sarfraz, M. Saquib and Roitberg, Alina and Stiefelhagen, Rainer}, booktitle = {In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024)}, doi = {https://doi.org/10.48550/arXiv.2409.17555}, eprint = {2409.17555}, eventtitle = {2024 Conference on Neural Information Processing Systems}, language = {en}, primaryclass = {cs.LG}, title = {Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler}, url = {https://arxiv.org/abs/2409.17555}, year = 2024 }
39. 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 }
40. 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 }
41. Qian, C., Manolache, A., Morris, C., & Niepert, M. (2024). Probabilistic Graph Rewiring via Virtual Nodes. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). https://doi.org/10.48550/arXiv.2405.17311
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  @inproceedings{qian2024probabilisticgraphrewiringvirtual, abstract = {Message-passing graph neural networks (MPNNs) have emerged as a powerful paradigm for graph-based machine learning. Despite their effectiveness, MPNNs face challenges such as under-reaching and over-squashing, where limited receptive fields and structural bottlenecks hinder information flow in the graph. While graph transformers hold promise in addressing these issues, their scalability is limited due to quadratic complexity regarding the number of nodes, rendering them impractical for larger graphs. Here, we propose implicitly rewired message-passing neural networks (IPR-MPNNs), a novel approach that integrates implicit probabilistic graph rewiring into MPNNs. By introducing a small number of virtual nodes, i.e., adding additional nodes to a given graph and connecting them to existing nodes, in a differentiable, end-to-end manner, IPR-MPNNs enable long-distance message propagation, circumventing quadratic complexity. Theoretically, we demonstrate that IPR-MPNNs surpass the expressiveness of traditional MPNNs. Empirically, we validate our approach by showcasing its ability to mitigate under-reaching and over-squashing effects, achieving state-of-the-art performance across multiple graph datasets. Notably, IPR-MPNNs outperform graph transformers while maintaining significantly faster computational efficiency.}, archiveprefix = {arXiv}, author = {Qian, Chendi and Manolache, Andrei and Morris, Christopher and Niepert, Mathias}, booktitle = {In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024)}, doi = {https://doi.org/10.48550/arXiv.2405.17311}, eprint = {2405.17311}, eventtitle = {2024 Conference on Neural Information Processing Systems}, language = {en}, primaryclass = {cs.LG}, title = {Probabilistic Graph Rewiring via Virtual Nodes}, url = {https://arxiv.org/abs/2405.17311}, year = 2024 }
42. 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 }
43. 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 }
44. 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 }
45. 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 }

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