Orateur
Prof.
Elisabeth GASSIAT
(LMO/Université Paris-Saclay)
Description
It is a common idea that high dimensional data (or features) may lie on low dimensional support making learning easier. In this talk, I will present a very general set-up in which it is possible to recover low dimensional non-linear structures with noisy data, the noise being totally unknown and possibly large.
Then I will present minimax rates for the estimation of the support in Hausdorff distance.
