Redshaw, J., Ting, D. S. J., Brown, A., Hirst, J. D., & Gärtner, T. (2023). Krein support vector machine classification of antimicrobial peptides. Digital Discovery. (doi) (reposiTUm)
Burgstaller-Muehlbacher, S., Crotty, S., Schmidt, H., Reden, F., Drucks, T., & von Haeseler, A. (2023). ModelRevelator: Fast phylogenetic model estimation via deep learning. Molecular Phylogenetics and Evolution, 188, Article 107905. (doi) (reposiTUm)
Haywood, A. L., Redshaw, J., Hanson-Heine, M. W. D., Taylor, A., Brown, A., Mason, A. M., Gärtner, T., & Hirst, J. D. (2022). Kernel Methods for Predicting Yields of Chemical Reactions. Journal of Chemical Information and Modeling, 62(9), 2077–2092. (doi) (reposiTUm)
Conference Proceedings Contributions
Bressan, M., Esposito, E., & Thiessen, M. (2024). Efficient Algorithms for Learning Monophonic Halfspaces in Graphs. In Proceedings of Machine Learning Research. 37th Annual Conference on Learning Theory (2024), Edmonton, Canada. (reposiTUm)
Bressan, M., Cesa-Bianchi, N., Esposito, E., Mansour, Y., Moran, S., & Thiessen, M. (2024). A Theory of Interpretable Approximations. In Proceedings of Thirty Seventh Conference on Learning Theory. 37th Annual Conference on Learning Theory, Edmonton, Canada. (reposiTUm)
Indri, P., Blohm, P., Athavale, A., Bartocci, E., Weissenbacher, G., Maffei, M., Nickovic, D., Gärtner, T., & Malhotra, S. (2024). Distillation based Robustness Verification with PAC Guarantees. In Volume 235: International Conference on Machine Learning, 21-27 July 2024, Vienna, Austria. 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria. (reposiTUm)
Chen, F., Weitkämper, F., & Malhotra, S. (2024). Understanding Domain-Size Generalization in Markov Logic Networks. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part VII (pp. 297–314). (doi) (reposiTUm)
Graziani, C., Drucks, T., Jogl, F., Bianchini, M., Scarselli, F., & Gärtner, T. (2024). The Expressive Power of Path-Based Graph Neural Networks. In Z. K. Ruslan Salakhutdinov Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, Felix Berkenkamp (Ed.), Proceedings of the 41st International Conference on Machine Learning. PMLR. http://hdl.handle.net/20.500.12708/199519 (reposiTUm)
Welke, P., Thiessen, M., Jogl, F., & Gärtner, T. (2023). Expectation-Complete Graph Representations with Homomorphisms. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of the 40th International Conference on Machine Learning (pp. 36910–36925). Proceedings of Machine Learning Research. (reposiTUm)
Toborek, V., Busch, M., Boßert, M., Bauckhage, C., & Welke, P. (2023). A New Aligned Simple German Corpus. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (pp. 11393–11412). Association for Computational Linguistics. (doi) (reposiTUm)
Jogl, F., Thiessen, M., & Gärtner, T. (2023). Expressivity-Preserving GNN Simulation. In Advances in Neural Information Processing Systems. 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, United States of America (the). (reposiTUm)
Bause, F., Jogl, F., Welke, P., & Thiessen, M. (2023). Maximally Expressive GNNs for Outerplanar Graphs. In The Second Learning on Graphs Conference (LoG 2023). Second Learning on Graphs Conference (LoG 2023), Austria. OpenReview.net. (doi) (reposiTUm)
Patrick Indri, Tamara Drucks, & Gärtner, T. (2023). Can stochastic weight averaging improve generalization in private learning? In ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models. ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models, Kigali, Rwanda. (doi) (reposiTUm)
Bause, F., Jogl, F., Indri, P., Drucks, T., Penz, D., Kriege, N., Gärtner, T., Welke, P., & Thiessen, M. (2023). Maximally Expressive GNNs for Outerplanar Graphs. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning. NeurIPS 2023 Workshop: New Frontiers in Graph Learning, New Orleans, LA, United States of America (the). OpenReview.net. (doi) (reposiTUm)
Brasoveanu, A. D., Jogl, F., Welke, P., & Thiessen, M. (2023). Extending Graph Neural Networks with Global Features. In The Second Learning on Graphs Conference (LoG 2023). The Second Learning on Graphs Conference (LoG 2023), online, Austria. OpenReview.net. (doi) (reposiTUm)
Daniele, A., Campari, T., Malhotra, S., & Serafini, L. (2023). Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23) (pp. 3597–3605). International Joint Conferences on Artificial Intelligence. (doi) (reposiTUm)
Müller, S., Toborek, V., Beckh, K., Jakobs, M., Bauckhage, C., & Welke, P. (2023). An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning. In D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis, & F. Bonchi (Eds.), Machine Learning and Knowledge Discovery in Databases: Research Track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III (pp. 462–478). Springer. (doi) (reposiTUm)
Graziani, C., Drucks, T., Bianchini, M., Scarselli, F., & Gärtner, T. (2023). No PAIN no Gain: More Expressive GNNs with Paths. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning. NeurIPS 2023 Workshop: New Frontiers in Graph Learning, New Orleans, LA, United States of America (the). OpenReview.net. (doi) (reposiTUm)
Lachi, V., Moallemy-Oureh, A., Roth, A., & Welke, P. (2023). Graph Pooling Provably Improves Expressivity. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning. NeurIPS 2023 Workshop: New Frontiers in Graph Learning, New Orleans, LA, United States of America (the). OpenReview.net. (doi) (reposiTUm)
Mohiuddin, K., Alam, M. A., Alam, M. M., Welke, P., Martin, M., Lehmann, J., & Vahdati, S. (2023). Retention is All You Need. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 4752–4758). (doi) (reposiTUm)
Sanchez, R., Conrads, L., Welke, P., Cvejoski, K., & Ojeda, C. (2023). Hidden Schema Networks. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 4764–4798). Association for Computational Linguistics. (doi) (reposiTUm)
Schedl, M., Brandl, S., Lesota, O., Parada-Cabaleiro, E., Penz, D., & Rekabsaz, N. (2022). LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis. In ACM SIGIR Conference on Human Information Interaction and Retrieval. ACM. (doi) (reposiTUm)
Ganhör, C., Penz, D., Rekabsaz, N., Lesota, O., & Schedl, M. (2022). Unlearning Protected User Attributes in Recommendations with Adversarial Training. In SIGIR ’22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2142–2147). (doi) (reposiTUm)
Moskalev, A., Sepliarskaia, A., Sosnovik, I., & Smeulders, A. (2022). LieGG: Studying Learned Lie Group Generators. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Advances in Neural Information Processing Systems 35 (NeurIPS 2022), New Orleans, United States of America (the). (reposiTUm)
Thiessen, M., & Gärtner, T. (2022). Online learning of convex sets on graphs. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022), Grenoble, France. (reposiTUm)
Indri, P., Bartoli, A., Medvet, E., & Nenzi, L. (2022). One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems. In EuroGP 2022: Genetic Programming (pp. 34–50). Springer-Verlag. (doi) (reposiTUm)
Bressan, M., Cesa-Bianchi, N., Lattanzi, S., Paudice, A., & Thiessen, M. (2022). Active Learning of Classifiers with Label and Seed Queries. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, Louisiana, United States of America (the). Neural information processing systems foundation. (doi) (reposiTUm)
Jogl, F., Thiessen, M., & Gärtner, T. (2022). Weisfeiler and Leman Return with Graph Transformations. In 18th International Workshop on Mining and Learning with Graphs - Accepted Papers. 18th International Workshop on Mining and Learning with Graphs, Grenoble, France. (doi) (reposiTUm)
Jogl, F., Thiessen, M., & Gärtner, T. (2022). Reducing Learning on Cell Complexes to Graphs. In ICLR 2022 Workshop on Geometrical and Topological Representation Learning. ICLR 2022 Workshop on Geometrical and Topological Representation Learning, Unknown. (doi) (reposiTUm)
Schmidt, D. (2022). Dojo: A Benchmark for Large Scale Multi-Task Reinforcement Learning. In ALOE 2022. Accepted Papers. Workshop on Agent Learning in Open-Endedness (ALOE) at ICLR 2022, Unknown. (doi) (reposiTUm)
Melchiorre, A. B., Penz, D., Ganhör, C., Lesota, O., Fragoso, V., Friztl, F., Parada-Cabaleiro, E., Schubert, F., & Schedl, M. (2022). EmoMTB: Emotion-aware Music Tower Blocks. In ICMR ’22: Proceedings of the 2022 International Conference on Multimedia Retrieval (pp. 206–210). (doi) (reposiTUm)
Krauck, A., Penz, D., & Schedl, M. (2021). Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User Features. In RecSysChallenge ’21: Proceedings of the Recommender Systems Challenge 2021. ACM. (doi) (reposiTUm)
Thiessen, M., & Gärtner, T. (2021). Active Learning of Convex Halfspaces on Graphs. In Advances in Neural Information Processing Systems 34. Advances in Neural Information Processing Systems 34. http://hdl.handle.net/20.500.12708/58787 (reposiTUm)
Thiessen, M., & Gärtner, T. (2021). Active Learning Convex Halfspaces on Graphs. In SubSetML @ ICML2021: Subset Selection in Machine Learning: From Theory to Practice. SubSetML: Subset Selection in Machine Learning: From Theory to Practice, Unknown. (doi) (reposiTUm)
Thiessen, M., & Gärtner, T. (2021). Active Learning of Convex Halfspaces on Graphs. In Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (pp. 1–13). (doi) (reposiTUm)
Thiessen, M., & Gärtner, T. (2020). Active Learning on Graphs with Geodesically Convex Classes. In Proceedings of 16th International Workshop on Mining and Learning with Graphs (MLG’20). 16th International Workshop on Mining and Learning with Graphs, Austria. (doi) (reposiTUm)
Schmied, T., & Thiessen, M. (2020). Efficient Reinforcement Learning via Self-supervised learning and Model-based methods. In Challenges of Real-World RL. NeurIPS 2020 Workshop. Accepted Papers. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. (doi) (reposiTUm)
Haywood, A. L., Redshaw, J., Gärtner, T., Taylor, A., Mason, A. M., & Hirst, J. D. (2020). Machine Learning for Chemical Synthesis. In H. M. Cartwright (Ed.), Machine Learning in Chemistry : The Impact of Artificial Intelligence (pp. 169–194). The Royal Society of Chemistry. (doi) (reposiTUm)