CORDIS Project
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This project focuses on developing a new theory of generalization in Machine Learning to better understand data requirements for various learning tasks. It addresses challenges related to algorithm efficiency and privacy, aiming to enhance the applicability of learning algorithms in real-world scenarios.
Recent years have witnessed tremendous progress in the field of Machine Learning (ML).
Learning algorithms are applied in an ever-increasing variety of contexts, ranging from engineering challenges such as self-driving cars all the way to societal contexts involving private data.
These developments pose important challenges (i) Many of the recent breakthroughs demonstrate phenomena that lack explanations, and sometimes even contradict conventional wisdom.
One main reason for this is because clas…
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