Machine learning over spaces of measures : invariant deep networks and quantile regression
This thesis proposes theoretical and numerical contributions to perform machine learning and statistics over the space of probability distributions. In a first part, we introduce a new class of neural network architectures to process probability measures in their Lagrangian form (obtained by sampling) as both inputs and outputs, which is characteri...
