DENEVI , Giulia

We study the Meta-Learning paradigm where the goal is to select an algorithm in a prescribed family – usually denoted as inner or within-task algorithm – that is appropriate to address a class of learning problems (tasks), sharing specific similarities. More precisely, we aim at designing a procedure, called meta-algorithm, that is able to...

We consider the inverse problem of retrieving aerosol extinction coefficients from Raman lidar measurements. In this problem the unknown and the data are related through the exponential of a linear operator, the unknown is non-negative and the data follow the Poisson distribution. Standard methods work on the log-transformed data and solve the...