Phase-Type Variational Autoencoders for Heavy-Tailed Data

Researchers propose Phase-Type Variational Autoencoders (PH-VAE), a new deep learning model that uses Phase-Type distributions to better capture heavy-tailed data patterns where extreme events are critical. The approach outperforms standard VAE models with Gaussian decoders in modeling tail behavior and extreme quantiles, marking the first integration of Phase-Type distributions into deep generative modeling.