3 papers
stat.ML2026
Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models
Abdelhakim Ziani, Andras Horvath, Paolo Ballarini
Heavy-tailed distributions are prevalent in performance evaluation, network traffic, and risk modeling. This behavior poses a fundamental challenge for modern deep generative model…
cs.LG2026
Phase-Type Variational Autoencoders for Heavy-Tailed Data
Abdelhakim Ziani, András Horváth, Paolo Ballarini
Heavy-tailed distributions are ubiquitous in real-world data, where rare but extreme events dominate risk and variability. However, standard Variational Autoencoders (VAEs) employ…
cs.PF2025
Approximating Heavy-Tailed Distributions with a Mixture of Bernstein Phase-Type and Hyperexponential Models
Abdelhakim Ziani, András Horváth, Paolo Ballarini
Heavy-tailed distributions, prevalent in a lot of real-world applications such as finance, telecommunications, queuing theory, and natural language processing, are challenging to m…