RNN-SURV: A Deep Recurrent Model for Survival Analysis. - Eleonora Giunchiglia; Anton Nemchenko; Mihaela van der Schaar (ICANN (3), 2018)

Conditional Behavior Trees: Definition, Executability, and Applications. - Eleonora Giunchiglia; Michele Colledanchise; Lorenzo Natale; Armando Tacchella (SMC, 2019)

Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods. - Oana-Maria Camburu; Eleonora Giunchiglia; Jakob N. Foerster; Thomas Lukasiewicz; Phil Blunsom (CoRR, 2019)

Knowledge Graph Extraction from Videos. - Louis Mahon; Eleonora Giunchiglia; Bowen Li 001; Thomas Lukasiewicz (ICMLA, 2020)

Coherent Hierarchical Multi-Label Classification Networks. - Eleonora Giunchiglia; Thomas Lukasiewicz (NeurIPS, 2020)

The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets. - Oana-Maria Camburu; Eleonora Giunchiglia; Jakob N. Foerster; Thomas Lukasiewicz; Phil Blunsom (CoRR, 2020)

Lightweight Visual Question Answering using Scene Graphs. - Sai Vidyaranya Nuthalapati; Ramraj Chandradevan; Eleonora Giunchiglia; Bowen Li 001; Maxime Kayser; Thomas Lukasiewicz; Carl Yang 001 (CIKM, 2021)

Multi-Label Classification Neural Networks with Hard Logical Constraints. - Eleonora Giunchiglia; Thomas Lukasiewicz (J. Artif. Intell. Res., 2021)

Deep Learning with Logical Constraints. - Eleonora Giunchiglia; Mihaela Catalina Stoian; Thomas Lukasiewicz (IJCAI, 2022)

To TTP or not to TTP?: Exploiting TTPs to Improve ML-based Malware Detection. - Yashovardhan Sharma; Eleonora Giunchiglia; Simon Birnbach; Ivan Martinovic (CSR, 2023)

Exploiting T-norms for Deep Learning in Autonomous Driving. - Mihaela C. Stoian; Eleonora Giunchiglia; Thomas Lukasiewicz (NeSy, 2023)

Machine Learning with Requirements: a Manifesto. - Eleonora Giunchiglia; Fergus Imrie; Mihaela van der Schaar; Thomas Lukasiewicz (CoRR, 2023)

ROAD-R: the autonomous driving dataset with logical requirements. - Eleonora Giunchiglia; Mihaela Catalina Stoian; Salman Khan 004; Fabio Cuzzolin; Thomas Lukasiewicz (Mach. Learn., 2023)

How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data. - Mihaela C. Stoian; Salijona Dyrmishi; Maxime Cordy; Thomas Lukasiewicz; Eleonora Giunchiglia (ICLR, 2024)

Deep Generative Models as an Adversarial Attack Strategy for Tabular Machine Learning. - Salijona Dyrmishi; Mihaela Catalina Stoian; Eleonora Giunchiglia; Maxime Cordy (ICMLC, 2024)

PiShield: A PyTorch Package for Learning with Requirements. - Mihaela C. Stoian; Alex Tatomir; Thomas Lukasiewicz; Eleonora Giunchiglia (IJCAI, 2024)

ULLER: A Unified Language for Learning and Reasoning. - Emile van Krieken; Samy Badreddine; Robin Manhaeve; Eleonora Giunchiglia (NeSy (1), 2024)

PiShield: A NeSy Framework for Learning with Requirements. - Mihaela Catalina Stoian; Alex Tatomir; Thomas Lukasiewicz; Eleonora Giunchiglia (CoRR, 2024)

ROAD-Waymo: Action Awareness at Scale for Autonomous Driving. - Salman Khan 004; Izzeddin Teeti; Reza Javanmard Alitappeh; Mihaela C. Stoian; Eleonora Giunchiglia; Gurkirt Singh; Andrew Bradley; Fabio Cuzzolin (CoRR, 2024)

CCN+: A neuro-symbolic framework for deep learning with requirements. - Eleonora Giunchiglia; Alex Tatomir; Mihaela Catalina Stoian; Thomas Lukasiewicz (Int. J. Approx. Reason., 2024)

Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints. - Mihaela C. Stoian; Eleonora Giunchiglia (ICLR, 2025)

A Survey on Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, and Beyond. - Mihaela Catalina Stoian; Eleonora Giunchiglia; Thomas Lukasiewicz (CoRR, 2025)

TRIDENT: Temporally Restricted Inference via DFA-Enhanced Neural Traversal. - Vincenzo Collura; Karim Tit; Laura Bussi; Eleonora Giunchiglia; Maxime Cordy (CoRR, 2025)