2024W

• Introduction to Machine Learning / 194.025 / VU
• Machine Learning Algorithms and Applications / 194.101 / PR
• Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning / 194.118 / SE
• Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU

• Maximally Expressive GNNs for Outerplanar Graphs / 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. https://doi.org/10.34726/5433
  Download: PDF (880 KB)
  Project: StruDL (2023–2027)
• Maximally Expressive GNNs for Outerplanar Graphs / Bause, F., Jogl, F., Indri, P., Drucks, T., Penz, D., Kriege, N., Gärtner, T., Welke, P., & Thiessen, M. (2023, December 1). Maximally Expressive GNNs for Outerplanar Graphs [Poster Presentation]. Learning-on-Graphs Conference 2023: Local Meetup, München, Germany. https://doi.org/10.34726/5344
  Downloads: Paper (880 KB) / Poster (422 KB)
  Project: StruDL (2023–2027)
• Unlearning Protected User Attributes in Recommendations with Adversarial Training / 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). https://doi.org/10.1145/3477495.3531820
• EmoMTB: Emotion-aware Music Tower Blocks / 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). https://doi.org/10.1145/3512527.3531351
• LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis / 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. https://doi.org/10.1145/3498366.3505791
• Recommending reviewers for theses using artificial intelligence / Penz, D. (2021). Recommending reviewers for theses using artificial intelligence [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.76463
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• Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User Features / 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. https://doi.org/10.1145/3487572.3487874

• Antibody-antigen binding affinity prediction through the use of geometric deep learning : a framework for binding affinity prediction with graph neural networks / Traxler, F. (2023). Antibody-antigen binding affinity prediction through the use of geometric deep learning : a framework for binding affinity prediction with graph neural networks [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.105645
  Download: PDF (4.87 MB)