2024W

• 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 for PhD Students / 194.110 / SE
• Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning / 194.118 / SE
• Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU

2025S

• Project in Computer Science 1 / 194.145 / PR
• Seminar for PhD Students / 194.110 / SE

• Understanding Domain-Size Generalization in Markov Logic Networks / 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). https://doi.org/10.1007/978-3-031-70368-3_18
• 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 Volume 235: International Conference on Machine Learning, 21-27 July 2024, Vienna, Austria. 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria.
• Logical Distillation of Graph Neural Networks / Pluska, A., Welke, P., Gärtner, T., & Malhotra, S. (2024). Logical Distillation of Graph Neural Networks. In ICML 2024 Workshop on Mechanistic Interpretability. 21st International Conference on Principles of Knowledge Representation and Reasoning, Hanoi, Viet Nam. https://doi.org/10.34726/7099
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  Project: StruDL (2023–2027)
• Simple and Effective Transfer Learning for Neuro-Symbolic Integration / Daniele, A., Campari, T., Malhotra, S., & Serafini, L. (2024). Simple and Effective Transfer Learning for Neuro-Symbolic Integration. In T. R. Besold, A. S. d’Avila Garcez, E. Jimenez-Ruiz, R. Confalonieri, P. Madhyastha, & B. Wagner (Eds.), Neural-Symbolic Learning and Reasoning (pp. 166–179). https://doi.org/10.34726/7321
• Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions / 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. https://doi.org/10.24963/ijcai.2023/400