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Appears in collection : Statistics and Machine Learning at Paris-Saclay (2023 Edition)

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.

Information about the video

  • Date of recording 09/03/2023
  • Date of publication 12/03/2023
  • Institution IHES
  • Language English
  • Audience Researchers
  • Format MP4

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