8 citations · 37 across the 26 of their papers we have counts for
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Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation
Guy Amir, Shahaf Bassan, Guy Katz
The ability to interpret Machine Learning (ML) models is becoming increasingly essential. However, despite significant progress in the field, there remains a lack of rigorous chara…
Safe and Reliable Training of Learning-Based Aerospace Controllers
Udayan Mandal, Guy Amir, Haoze Wu +10
In recent years, deep reinforcement learning (DRL) approaches have generated highly successful controllers for a myriad of complex domains. However, the opaque nature of these mode…
Verifying the Generalization of Deep Learning to Out-of-Distribution Domains
Guy Amir, Osher Maayan, Tom Zelazny +2
Deep neural networks (DNNs) play a crucial role in the field of machine learning, demonstrating state-of-the-art performance across various application domains. However, despite th…
Verification-Guided Shielding for Deep Reinforcement Learning
Davide Corsi, Guy Amir, Andoni Rodriguez +3
In recent years, Deep Reinforcement Learning (DRL) has emerged as an effective approach to solving real-world tasks. However, despite their successes, DRL-based policies suffer fro…
Local vs. Global Interpretability: A Computational Complexity Perspective
Shahaf Bassan, Guy Amir, Guy Katz
The local and global interpretability of various ML models has been studied extensively in recent years. However, despite significant progress in the field, many known results rema…
Shield Synthesis for LTL Modulo Theories
Andoni Rodriguez, Guy Amir, Davide Corsi +2
In recent years, Machine Learning (ML) models have achieved remarkable success in various domains. However, these models also tend to demonstrate unsafe behaviors, precluding their…
Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates
Udayan Mandal, Guy Amir, Haoze Wu +9
Deep reinforcement learning (DRL) is a powerful machine learning paradigm for generating agents that control autonomous systems. However, the ``black box'' nature of DRL agents lim…
Analyzing Adversarial Inputs in Deep Reinforcement Learning
Davide Corsi, Guy Amir, Guy Katz +1
In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-world and complex systems. However,…
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
Haoze Wu, Omri Isac, Aleksandar Zeljić +14
This paper serves as a comprehensive system description of version 2.0 of the Marabou framework for formal analysis of neural networks. We discuss the tool's architectural design a…