CORDIS Project
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This project addresses challenges in unsupervised learning within artificial intelligence, focusing on developing efficient algorithms to identify patterns in unlabelled data. It aims to explore the trade-offs between statistical performance and computational efficiency, particularly in active learning scenarios.
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: f…
UNIVERSITAET POTSDAM
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