Deep generative models are statistical models that leverage recent advances deep learning. The most well-known examples include variational autoencoders (VAEs) and generative adversarial networks (GANs). We will briefly review them and see how they can be use to impute missing values in incomplete data sets.
Pierre-Alexandre Mattei is a Research Scientist at Inria. He is part of the Maasai (Models and Algorithms for Artificial Intelligence) team and is also affiliated with the J.A. Dieudonné lab. His field of research is statistical machine learning, with a particular emphasis on hidden variables and model uncertainty. During his Ph.D, he mainly developed new Bayesian model selection methods for high-dimensional data. He is also currently working on deep generative models and their applications. He is one of the co-organisers of the Workshop on the Art of Learning with Missing Values (Artemiss).
The presentation will be in English and streamed on BBB