Title: Deconvolution with unknown noise distribution is possible. Application to distribution and support learning

Abstract: 

I consider the deconvolution problem in the case where no information is known about the noise distribution. More precisely, no assumption is made on the noise distribution and no samples are available to estimate it: the deconvolution problem is solved based only on observations of the corrupted signal. I will present an identifiability result, and its use for statistical inference in various applications.