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pro vyhledávání: '"Ocello, Antonio"'
Score-based generative models (SGMs) aim at estimating a target data distribution by learning score functions using only noise-perturbed samples from the target.Recent literature has focused extensively on assessing the error between the target and e
Externí odkaz:
http://arxiv.org/abs/2402.04650
Autor:
Kharroubi, Idris, Ocello, Antonio
This article explores an optimal stopping problem for branching diffusion processes. It consists in looking for optimal stopping lines, a type of stopping time that maintains the branching structure of the processes under analysis. By using a dynamic
Externí odkaz:
http://arxiv.org/abs/2401.12811
Autor:
Ocello, Antonio
This paper introduces the formalism required to analyze a certain class of stochastic control problems that involve a super diffusion as the underlying controlled system. To establish the existence of these processes, we show that they are weak scali
Externí odkaz:
http://arxiv.org/abs/2306.15962
Autor:
Ocello, Antonio
Our focus is on the study of optimal control problem for branching diffusion processes. Initially, we introduce the problem in its strong formulation and expand it to include linearly growing drifts. To ensure its proper definition, we establish boun
Externí odkaz:
http://arxiv.org/abs/2304.07064
Autor:
Kharroubi, Idris, Ocello, Antonio
We consider an optimal stochastic target problem for branching diffusion processes. This problem consists in finding the minimal condition for which a control allows the underlying branching process to reach a target set at a finite terminal time for
Externí odkaz:
http://arxiv.org/abs/2206.13267
Autor:
Kharroubi, Idris, Ocello, Antonio
Publikováno v:
In Stochastic Processes and their Applications April 2024 170
