2025W

• Bachelor Thesis in Computer Science / 194.112 / PR
• Introduction to Machine Learning / 194.025 / VU
• Neurosymbolic Reasoning / 192.173 / VU
• Project in Computer Science 1 / 194.145 / PR
• Scientific Research and Writing / 193.052 / SE
• Seminar for PhD Students / 194.110 / SE
• Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU
• Theoretical Foundations and Research Topics in Machine Learning / 194.201 / VU

2026S

• Project in Computer Science 1 / 194.145 / PR
• Scientific Research and Writing / 193.052 / SE
• Seminar for PhD Students / 194.110 / SE

• Prime Implicant Explanations for Reaction Feasibility Prediction / Weinbauer, K., Phan, T.-L., Stadler, P. F., Gärtner, T., & Malhotra, S. (2025). Prime Implicant Explanations for Reaction Feasibility Prediction. https://doi.org/10.48550/ARXIV.2510.09226
  Project: StruDL (2023–2027)
• On Local Limits of Sparse Random Graphs: Color Convergence and the Refined Configuration Model / Pluska, A., & Malhotra, S. (2025). On Local Limits of Sparse Random Graphs: Color Convergence and the Refined Configuration Model. In 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025). 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025), San Diego, United States of America (the). Curran Associates, Inc.
  Project: VASSAL (2024–2027)
• Probably Approximately Global Robustness Certification / Blohm, P., Indri, P., Gärtner, T., & Malhotra, S. (2025). Probably Approximately Global Robustness Certification. In Proceedings of 42nd International Conference on Machine Learning (ICML 2025). 42nd International Conference on Machine Learning (ICML 2025), Vancouver, Canada.
  Projects: NanoX (2024–2028) / StruDL (2023–2027)
• Lifted inference beyond first-order logic / Malhotra, S., Bizzaro, D., & Serafini, L. (2025). Lifted inference beyond first-order logic. Artificial Intelligence, 342, Article 104310. https://doi.org/10.1016/j.artint.2025.104310
• Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective / Azzolin, S., Malhotra, S., Passerini, A., & Teso, S. (2025). Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective. In A. Singh, M. Fazel, D. Hsu, S. Lacoste-Julien, F. Berkenkamp, T. Maharaj, K. Wagstaff, & J. Zhu (Eds.), Volume 267: International Conference on Machine Learning, 13-19 July 2025, Vancouver Convention Center, Vancouver, Canada. http://hdl.handle.net/20.500.12708/224837
  Project: NanoX (2024–2028)
• 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 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
• 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. ICML 2024 Workshop on Mechanistic Interpretability, Vienna, Austria. https://doi.org/10.34726/7099
  Download: PDF (309 KB)
  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
  Download: PDF (1 MB)
• Logical Distillation of Graph Neural Networks / Pluska, A., Welke, P., Gärtner, T., & Malhotra, S. (2024). Logical Distillation of Graph Neural Networks. In P. Marquis, M. Ortiz, & M. Pagnucco (Eds.), Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning (pp. 920–930). IJCAI Organization. https://doi.org/10.24963/kr.2024/86
  Download: PDF (213 KB)
  Projects: NanoX (2024–2028) / StruDL (2023–2027)
• 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

• Probabilistic verification of black-box systems / Blohm, P. (2025). Probabilistic verification of black-box systems [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.128785
  Download: PDF (14.8 MB)