CORDIS Project
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This project focuses on improving the interpretability and reliability of machine learning models. By integrating statistical methods with machine learning, it aims to provide clearer error bounds and enhance the safety of predictive systems in various applications.
Recent breakthroughs in machine learning (ML) have brought about a transformative impact on decision-making, autonomous systems, medical diagnosis, and creation of new scientific knowledge.
However, this progress has a major drawback: modern predictive systems are extremely complex and hard to interpret, a problem known as the black-box effect.
The opaque nature of modern ML models, trained on increasingly diverse, incomplete, and noisy data, and later deployed in varying environments, hinders o…
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