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The workshop is part of the thematic program on Data Assimilation. It is a follow up meeting from the one held last spring at Caltech.

The meeting will bring together researchers interested in the computational and mathematical aspects of the inverse problems that arise in machine learning. These include:

  • regularization and inverse problems in machine learning;
  • large scale and stochastic optimization in machine learning;
  • large scale kernel methods;
  • Laplacian and p-Laplacian regularization for semi-supervised learning;

as well as connection with inverse problem theory.