Year: 2023
Type: article
Abstract: The missing mathematical story of Bayesian uncertainty quantification for big data New machine- and statistical learning methods are being developed to process the ever-increasing amount of available information. However, these methods often behave like black-box procedures without any theoretical underpinning. In this project, I will derive theoretical guarantees but also limitations of such procedures and, based on their mathe-matical understanding, increase their accuracy in complex models.
Source: The Project Repository Journal
DOI: 10.54050/prj1720274
Author: Botond Szabo
Institution: Bocconi University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4383817496