ERC Consolidator Grant · 2025
Unsupervised learning is a key problem of artificial intelligence, at the crossroad of statistics and machine learning. The aim is to infer patterns from unlabelled data, by providing learning algorithms that are computationally efficient - i.e. polynomial time - and statistically performant - i.e. minimising an error criterion - and by characterising the fundamental limits for learning. In the last decade, deep and important phenomena of statistical-computational trade-offs have been unveiled: for some canonical vanilla problems, it is now admitted that no algorithm is both statistically optimal and computationally efficient. However, and somewhat surprisingly, many extensions of these…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.