2024W

• 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

2025S

• 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

• GraphPrivatizer: Improved Structural Differential Privacy for Graph Neural Networks / Joshi, R. B., Indri, P., & Mishra, S. (2024). GraphPrivatizer: Improved Structural Differential Privacy for Graph Neural Networks. Transactions on Machine Learning Research.
• Distillation based Robustness Verification with PAC Guarantees / 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 International Conference on Machine Learning 2024 - Next Generation of AI Safety Workshop. International Conference on Machine Learning 2024 - Next Generation of AI Safety Workshop, Vienna, Austria. http://hdl.handle.net/20.500.12708/200890
• 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)
• Can stochastic weight averaging improve generalization in private learning? / 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. https://doi.org/10.34726/5349
  Download: Main paper (366 KB)
• One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems / 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. https://doi.org/10.1007/978-3-031-02056-8_3