Neural Network Verification for the Masses (of AI graduates). - Ekaterina Komendantskaya; Rob Stewart 001; Kirsty Duncan; Daniel Kienitz; Pierre Le Hen; Pascal Bacchus (CoRR, 2019)

Neural Networks, Secure by Construction - An Exploration of Refinement Types. - Wen Kokke; Ekaterina Komendantskaya; Daniel Kienitz; Robert Atkey; David Aspinall 001 (APLAS, 2020)

Continuous Verification of Machine Learning: a Declarative Programming Approach. - Ekaterina Komendantskaya; Wen Kokke; Daniel Kienitz (PPDP, 2020)

Property-driven Training: All You (N)Ever Wanted to Know About. - Marco Casadio; Matthew L. Daggitt; Ekaterina Komendantskaya; Wen Kokke; Daniel Kienitz; Rob Stewart 001 (CoRR, 2021)

The Effect of Manifold Entanglement and Intrinsic Dimensionality on Learning. - Daniel Kienitz; Ekaterina Komendantskaya; Michael A. Lones (AAAI, 2022)

Comparing Complexities of Decision Boundaries for Robust Training: A Universal Approach. - Daniel Kienitz; Ekaterina Komendantskaya; Michael A. Lones (ACCV (6), 2022)

Why Robust Natural Language Understanding is a Challenge. - Marco Casadio; Ekaterina Komendantskaya; Verena Rieser; Matthew L. Daggitt; Daniel Kienitz; Luca Arnaboldi 001; Wen Kokke (CoRR, 2022)

ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification. - Marco Casadio; Luca Arnaboldi 001; Matthew L. Daggitt; Omri Isac; Tanvi Dinkar; Daniel Kienitz; Verena Rieser; Ekaterina Komendantskaya (FoMLAS@CAV, 2023)