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Sagar Malhotra

Univ.Ass. / MSc PhD

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2025W

2026S

 

  • 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)
  • 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
  • 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