David Penz
Univ.Ass. Dipl.-Ing. / B.A.
Role
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PreDoc Researcher
Machine Learning, E194-06
Contact
- david.penz@tuwien.ac.at
- +43-1-58801-194604
- Erzherzog-Johann-Platz 1, Room FB0213
- vCard from TISS
Courses
2023W
- Bachelor Thesis in Computer Science / 194.112 / PR
- Introduction to Machine Learning / 194.025 / VU
- Machine Learning Algorithms and Applications / 194.101 / PR
- Project in Computer Science 1 / 194.145 / PR
- Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning / 194.118 / SE
- Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU
Publications
- 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
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Recommending reviewers for theses using artificial intelligence
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Penz, D. (2021). Recommending reviewers for theses using artificial intelligence [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.76463
Download: PDF (2.07 MB) - 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
Supervisions
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Antibody-Antigen Binding Affinity Prediction through the use of geometric deep learning
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Traxler, F. (2023). Antibody-Antigen Binding Affinity Prediction through the use of geometric deep learning [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.105645
Download: PDF (4.87 MB)