AAII Seminar: February 26th, 2020

Abstract: Of all machine learning methods generative models are particularly interesting for scientific applications because of their probabilistic nature and ability to fit complex data and probability distributions. However, in their vanilla form generative models have a number of shortcomings and failure modes which can be a hindrance to their application: They can be difficult to train on high dimensional data and they can fail in crucial tasks such as outlier detection, correct uncertainty estimation or the generation of realistic artificial data. In my talk I am going to explore the reasons for these failures and propose new generative models and generative model based approaches that are robust to these shortcomings. The proposed approaches are easy to train and validate, numerically stable and do not require fine-tuning. They should thus be particularly fitting for scientific applications. I will demonstrate how these approaches can be used for scientifically relevant tasks such as realistic data generation, probabilistic inference on corrupted data and outlier detection.