Enhancing Monte Carlo integration by control variates and statistical learning

Le 18 octobre: Aigerim ZHUMAN défendra sa thèse pour l’obtention du grade académique de Doctorat en Sciences

 

ISBA – Soutenance publique de thèse – Aigerim ZHUMAN

Titre : Enhancing Monte Carlo integration by control variates and statistical learning

Promoteur : Prof. Benoit Desguin (UCLouvain)

Date et lieu : vendredi 18 octobre 2024 à 15h30 – Auditoire DOYEN 31 – Place des Doyens – B-1348 Louvain-la-Neuve

This thesis focuses on enhancing Monte Carlo integration through the use of control variates, a variance reduction technique involving special functions with known integrals. Such techniques become crucial when the sampling process is complex or the integrand is computationally intensive. First, control variates are combined with adaptive importance sampling, another variance reduction method applicable to inference in Bayesian formulations of the linear and logistic regression models. Then the focus is on improving the standard Monte Carlo procedure by constructing control variates using the nearest neighbor approach. The proposed method finds applications in option pricing and optimal transport problems. In the next project, the control variate technique is applied to calculating the Sliced-Wasserstein distance, represented as an integral over the unit sphere, using spherical harmonics as control variates. This approach is applied to the image classification problem. Finally, control variates are built using the random forest method, different types of random forests being compared.

Jury members :

  • Prof. Johan Segers (UCLouvain) (Supervisor)
  • Prof. Rainer von Sachs (UCLouvain) (Chairperson)
  • Prof. Eugen Pircalabelu (UCLouvain)
  • Prof. François Portier (ENSAI, France)
  • Prof. Philippe Lambert (ULiège)

Pay attention : the public defense of Aigerim ZHUMAN will also take place in the form of a videoconference

Publié le 14 octobre 2024