Výsledky vyhledávání - "Bouchoucha, Rached"

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Toward Debugging Deep Reinforcement Learning Programs with RLExplorer

Autor: Bouchoucha, Rached, Yahmed, Ahmed Haj, Patil, Darshan, Rajendran, Janarthanan, Nikanjam, Amin, Chandar, Sarath, Khomh, Foutse

Deep reinforcement learning (DRL) has shown success in diverse domains such as robotics, computer games, and recommendation systems. However, like any other software system, DRL-based software systems are susceptible to faults that pose unique challe

Externí odkaz: http://arxiv.org/abs/2410.04322

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An Intentional Forgetting-Driven Self-Healing Method For Deep Reinforcement Learning Systems

Autor: Yahmed, Ahmed Haj, Bouchoucha, Rached, Braiek, Houssem Ben, Khomh, Foutse

Deep reinforcement learning (DRL) is increasingly applied in large-scale productions like Netflix and Facebook. As with most data-driven systems, DRL systems can exhibit undesirable behaviors due to environmental drifts, which often occur in constant

Externí odkaz: http://arxiv.org/abs/2308.12445

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Quality Issues in Machine Learning Software Systems

Autor: Côté, Pierre-Olivier, Nikanjam, Amin, Bouchoucha, Rached, Basta, Ilan, Abidi, Mouna, Khomh, Foutse

Context: An increasing demand is observed in various domains to employ Machine Learning (ML) for solving complex problems. ML models are implemented as software components and deployed in Machine Learning Software Systems (MLSSs). Problem: There is a

Externí odkaz: http://arxiv.org/abs/2306.15007

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Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?

Autor: Badran, Khaled, Côté, Pierre-Olivier, Kolopanis, Amanda, Bouchoucha, Rached, Collante, Antonio, Costa, Diego Elias, Shihab, Emad, Khomh, Foutse

As machine learning (ML) systems get adopted in more critical areas, it has become increasingly crucial to address the bias that could occur in these systems. Several fairness pre-processing algorithms are available to alleviate implicit biases durin

Externí odkaz: http://arxiv.org/abs/2212.02614

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