6 papers
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…
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…
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…
Statistical process discovery
Pierre Cry, Paolo Ballarini, András Horváth +1
Stochastic process discovery is concerned with deriving a model capable of reproducing the stochastic character of observed executions of a given process, stored in a log. This lea…
Probabilistic Process Discovery with Stochastic Process Trees
András Horváth, Paolo Ballarini, Pierre Cry
In order to obtain a stochastic model that accounts for the stochastic aspects of the dynamics of a business process, usually the following steps are taken. Given an event log, a p…
A framework for optimisation based stochastic process discovery
Pierre Cry, András Horváth, Paolo Ballarini +1
Process mining is concerned with deriving formal models capable of reproducing the behaviour of a given organisational process by analysing observed executions collected in an even…