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Modern Challenges in Learning Theory

CORDIS · observation · Publication date unknown

Modern Challenges in Learning Theory 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 classical ML theory adopts a worst-case perspective which is too pessimistic to explain practical ML: in reality data is rarely worst-case, and experiments indicate that often much less data is needed than predicted by traditional theory. (ii) The increase in ML applications that involve private and sensitive data highlights the need for algorithms that handle the data responsibly. While this need has been addressed by the field of Differential Privacy (DP), the c

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award

status

SIGNED

region

EU

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1433750

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EUR