Towards Scalable Verification of Deep Reinforcement Learning. - Guy Amir; Michael Schapira; Guy Katz (FMCAD, 2021)

Use and Perceptions of Multi-Monitor Workstations: A Natural Experiment. - Guy Amir; Ayala Prusak; Tal Reiss; Nir Zabari; Dror G. Feitelson (SER&IP@ICSE, 2021)

An SMT-Based Approach for Verifying Binarized Neural Networks. - Guy Amir; Haoze Wu 001; Clark W. Barrett; Guy Katz (TACAS (2), 2021)

Towards Scalable Verification of RL-Driven Systems. - Guy Amir; Michael Schapira; Guy Katz (CoRR, 2021)

Neural Network Robustness as a Verification Property: A Principled Case Study. - Marco Casadio; Ekaterina Komendantskaya; Matthew L. Daggitt; Wen Kokke; Guy Katz; Guy Amir; Idan Refaeli (CAV (1), 2022)

Verification-Aided Deep Ensemble Selection. - Guy Amir; Tom Zelazny; Guy Katz; Michael Schapira (FMCAD, 2022)

Scenario-assisted Deep Reinforcement Learning. - Raz Yerushalmi; Guy Amir; Achiya Elyasaf; David Harel; Guy Katz; Assaf Marron (MODELSWARD, 2022)

Verification-Aided Deep Ensemble Selection. - Guy Amir; Guy Katz; Michael Schapira (CoRR, 2022)

Constrained Reinforcement Learning for Robotics via Scenario-Based Programming. - Davide Corsi; Raz Yerushalmi; Guy Amir; Alessandro Farinelli; David Harel; Guy Katz (CoRR, 2022)

Verifying Generalization in Deep Learning. - Guy Amir; Osher Maayan; Tom Zelazny; Guy Katz; Michael Schapira (CAV (2), 2023)

veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System. - Guy Amir; Ziv Freund; Guy Katz; Elad Mandelbaum; Idan Refaeli (FM, 2023)

Formally Explaining Neural Networks within Reactive Systems. - Shahaf Bassan; Guy Amir; Davide Corsi; Idan Refaeli; Guy Katz (FMCAD, 2023)

Verifying Learning-Based Robotic Navigation Systems. - Guy Amir; Davide Corsi; Raz Yerushalmi; Luca Marzari; David Harel; Alessandro Farinelli; Guy Katz (TACAS (1), 2023)

Enhancing Deep Reinforcement Learning with Scenario-Based Modeling. - Raz Yerushalmi; Guy Amir; Achiya Elyasaf; David Harel; Guy Katz; Assaf Marron (SN Comput. Sci., 2023)

Marabou 2.0: A Versatile Formal Analyzer of Neural Networks. - Haoze Wu 001; Omri Isac; Aleksandar Zeljic; Teruhiro Tagomori; Matthew L. Daggitt; Wen Kokke; Idan Refaeli; Guy Amir; Kyle Julian; Shahaf Bassan; Pei Huang 002; Ori Lahav 002; et al. (CAV (2), 2024)

Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation. - Guy Amir; Shahaf Bassan; Guy Katz (ECAI, 2024)

Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates. - Udayan Mandal; Guy Amir; Haoze Wu 001; Ieva Daukantas; Fletcher Lee Newell; Umberto J. Ravaioli; Baoluo Meng; Michael Durling; Milan Ganai; Tobey Shim; Guy Katz; Clark W. Barrett (FMCAD, 2024)

Local vs. Global Interpretability: A Computational Complexity Perspective. - Shahaf Bassan; Guy Amir; Guy Katz (ICML, 2024)

Enforcing Specific Behaviours via Constrained DRL and Scenario-Based Programming. - Davide Corsi; Raz Yerushalmi; Guy Amir; Alessandro Farinelli; David Harel; Guy Katz (ICONIP (11), 2024)

Verification-Guided Shielding for Deep Reinforcement Learning. - Davide Corsi; Guy Amir; Andoni Rodríguez; Guy Katz; César Sánchez 001; Roy Fox (RLJ, 2024)

Analyzing Adversarial Inputs in Deep Reinforcement Learning. - Davide Corsi; Guy Amir; Guy Katz; Alessandro Farinelli (CoRR, 2024)

Safe and Reliable Training of Learning-Based Aerospace Controllers. - Udayan Mandal; Guy Amir; Haoze Wu 001; Ieva Daukantas; Fletcher Lee Newell; Umberto J. Ravaioli; Baoluo Meng; Michael Durling; Kerianne Hobbs; Milan Ganai; Tobey Shim; Guy Katz; et al. (CoRR, 2024)

Verifying the Generalization of Deep Learning to Out-of-Distribution Domains. - Guy Amir; Osher Maayan; Tom Zelazny; Guy Katz; Michael Schapira (J. Autom. Reason., 2024)

Shield Synthesis for LTL Modulo Theories. - Andoni Rodríguez; Guy Amir; Davide Corsi; César Sánchez 001; Guy Katz (AAAI, 2025)

What makes an Ensemble (Un) Interpretable? - Shahaf Bassan; Guy Amir; Meirav Zehavi; Guy Katz (CoRR, 2025)