Alireza Koochali

"The application of generative models in probabilistic machine learning"

( MPI-SWS talk in Kooperation mit dem Fachbereich Informatik)

In many sensitive domains like finance, health care or climate prediction, it is viable to determine the statistical model uncertainty. Probabilistic machine learning aims to learn from data while quantifying model uncertainty.On the other hand, Generative models are a class of statistical methods that can learn an unknown probability distribution from its samples to generate artificial data. Recently, with the introduction of neural network-based generative models like Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs), this field has drawn a lot of attention. However, most of the efforts are focused on generating realistic artificial data.In my Ph.D., I aim to investigate the possibility and the extent of utilizing the generative models' power for probabilistic machine learning.In this presentation, I am going to address my research in the direction of probabilistic forecasting and also and present my recent publication on one step ahead probabilistic forecasting using GANs. Finally, I will discuss the further direction of research on using generative models for probabilistic machine learning.

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