Séance Séminaire

Séminaire de Probabilités et Statistique

Learning Structures from High-dimensional Data

Machine learning plays an important role in artificial intelligence and data science. One core problem in learning is to characterize learnable structures from the high-dimensional data, which are formed by a large number of variables. This is a particularly challenging problem due to statistical and computational limitations. I shall present my contributions to the following three basic questions: What are structures of data? Can certain structures of data be learnt? How to learn? I shall present maximum-entropy models to characterize coherent structures of high-dimensional data such as textures and turbulent flows. This modeling problem is then closely related to deep learning for recognizing images and to transform learning for inverse problems such as source separation. Lastly, I present distributed and stochastic optimization algorithms to address large-scale learning problems.

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