Phase-Type Variational Autoencoders for Heavy-Tailed Data
Heavy-tailed distributions are ubiquitous in real-world data, where rare but extreme events dominate risk and variability. However, standard Variational Autoencoders (VAEs) employ simple decoder distributions, such as Gaussian distributions, that fail to capture heavy-tailed behavior, while existing heavy-tail-aware extensions remain restricted to predefined parametric families whose tail behavior is fixed a priori. We propose the Phase-Type Variational Autoencoder (PH-VAE), whose decoder distribution is a latent-conditioned Phase-Type (PH) distribution, defined as the absorption time of a continuous-time Markov chain (CTMC). This formulation composes multiple exponential time scales, yie...
| Authors | Abdelhakim Ziani, András Horváth, Paolo Ballarini |
|---|---|
| Primary category | cs.LG |
| Published | 2 March 2026 |
| Last updated at source | 26 May 2026 |
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