Verifiably Safe and Correct Deep Neural Networks

Project Information

VeriDeL

Grant agreement ID: 101112713

DOI

10.3030/101112713

EC signature date 25 October 2023

Start date 1 November 2023

End date 31 October 2028

Funded under

• European Research Council (ERC)

Total cost

€ 1 500 000,00

EU contribution

€ 1 500 000,00

Coordinated by

THE HEBREW UNIVERSITY OF JERUSALEM
Israel

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Conference proceedings (14)

What makes an Ensemble (Un) Interpretable?

Author(s): S. Bassan, G. Amir, M. Zehavi and G. Katz
Published in: Proc. 42nd Int. Conf. on Machine Learning (ICML), 2025
Publisher: ICML

Analyzing Adversarial Inputs in Deep Reinforcement Learning

Author(s): D. Corsi, G. Amir, G. Katz and A. Farinelli
Published in: Proc. 3rd Int. Conf. on Bridging the Gap Between AI and Reality (AISoLA), 2025
Publisher: AISoLA

Verification-Guided Shielding for Deep Reinforcement Learning

Author(s): D. Corsi, G. Amir, A. Rodriguez, C. Sanchez, G. Katz and R. Fox
Published in: Proc. 1st Reinforcement Learning Conf. (RLC), 2024
Publisher: RLC

A Certified Proof Checker for Deep Neural Network Verification in Imandra

Author(s): R. Desmartin, O. Isac, G. Passmore, E. Komendantskaya, K. Stark and G. Katz
Published in: Proc. 16th Int. Conf. on Interactive Theorem Proving (ITP), 2025
Publisher: ITP

Exploring and Evaluating Interplays of BPpy with Deep Reinforcement Learning and Formal Methods

Author(s): T. Yaacov, G. Weiss, A. Ashrov, G. Katz and H. Zisser
Published in: Proc. 20th Int. Conf. on Evaluation of Novel Approaches to Software Engineering (ENASE), 2025
Publisher: ENASE

Abstraction-Based Proof Production in Formal Verification of Neural Networks

Author(s): Y. Elboher, O. Isac, G. Katz, T. Ladner and H.Wu
Published in: Proc. 8th Int. Symposium on AI Verification (SAIV), 2025
Publisher: SAIV

On the Computational Tractability of the (Many) Shapley Values

Author(s): R. Marzouk, S. Bassan, G. Katz and C. de la Higuera
Published in: Proc. 28th Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 2025
Publisher: AISTATS

Local vs. Global Interpretability: A Computational Complexity Perspective

Author(s): S. Bassan, G. Amir and G. Katz
Published in: Proc. 41st Int. Conf. on Machine Learning (ICML), 2024
Publisher: ICML

On Augmenting Scenario-Based Modeling with Generative AI

Author(s): D. Harel, G. Katz, A. Marron and S. Szekely
Published in: Proc. 12th Int. Conf. on Model-Driven Engineering and Software Development (MODELSWARD), 2024
Publisher: Modelsward

Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation

Author(s): G. Amir, S. Bassan, and G. Katz
Published in: Proc. 27th European Conf. on Artificial Intelligence (ECAI), 2024
Publisher: ECAI

Marabou 2.0: A Versatile Formal Analyzer of Neural Networks

Author(s): H. Wu, O. Isac, A. Zeljic, T. Tagomori, M. Daggitt, W. Kokke, I. Refaeli, G. Amir, K. Julian, S. Bassan, P. Huang, O. Lahav, M. Wu, M. Zhang, E. Komendantskaya, G. Katz and C. Barrett
Published in: Proc. 36th Int. Conf. on Computer Aided Verification (CAV), pp. 249-264, 2024
Publisher: CAV

Neural Network Verification is a Programming Language Challenge

Author(s): L. Cordeiro, M. Daggitt, J. Girard-Satabin, O. Isac, T. Johnson, G. Katz, E. Komendantskaya, A. Lemesle, E. Manino, A. Sinkarovs and H. Wu
Published in: Proc. 34th European Symposium on Programming (ESOP), 2025
Publisher: ESOP

Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations

Author(s): S. Bassan, Y. Elboher, T. Ladner, M. Althoff and G. Katz
Published in: Proc. 42nd Int. Conf. on Machine Learning (ICML), 2025
Publisher: ICML

DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace

Author(s): G. Katz, N. Levy, I. Refaeli and R. Yerushalmi
Published in: Proc. 43rd Digital Avionics Systems Conf. (DASC), 2024
Publisher: DASC