Mihaela C Stoian and Eleonora Giunchiglia. Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints. In The Thirteenth International Conference on Learning Representations (ICLR), 2025.
van Krieken, E., Badreddine, S., Manhaeve, R., and Giunchiglia, E. Uller: A unified language for learning and reasoning. In International Conference on Neural-Symbolic Learning and Reasoning, pp. 219–239. Springer, 2024.
Mihaela C. Stoian, Eleonora Giunchiglia, and Thomas Lukasiewicz. Exploiting t-norms for deep learning in autonomous driving. In Proceedings of the International Workshop on Neural- Symbolic Learning and Reasoning, 2023.
Mihaela C˘at˘alina Stoian, Alex Tatomir, Thomas Lukasiewicz, and Eleonora Giunchiglia. PiShield: A PyTorch Package for Learning with Requirements. Proceedings of IJCAI, 2024.
Mihaela C. Stoian, Salijona Dyrmishi, Maxime Cordy, Thomas Lukasiewicz, and Eleonora Giunchiglia. How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data. In Proceedings of International Conference on Learning Representations, 2024.
Eleonora Giunchiglia, Alex Tatomir, Mihaela Cǎtǎlina Stoian, and Thomas Lukasiewicz. CCN+: A neuro-symbolic framework for deep learning with requirements. International Journal of Approximate Reasoning, page 109-124, 2024.
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA, USA
Talk at London School of Testing, London, UK
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley, CA, USA
Talk at UC San Francisco, Department of Testing, San Francisco, CA, USA