ERC Starting Grant · 2021
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…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.