2 citations · 2 across the 7 of their papers we have counts for
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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,…
On Improving Deep Active Learning with Formal Verification
Jonathan Spiegelman, Guy Amir, Guy Katz
Deep Active Learning (DAL) aims to reduce labeling costs in neural-network training by prioritizing the most informative unlabeled samples for annotation. Beyond selecting which sa…
What makes an Ensemble (Un) Interpretable?
Shahaf Bassan, Guy Amir, Meirav Zehavi +1
Ensemble models are widely recognized in the ML community for their limited interpretability. For instance, while a single decision tree is considered interpretable, ensembles of t…
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…
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…