VIDEO Uncertainty
In the world of big data, when there’s the need to estimate many parameters in very complex models making use of the large amount of available information, computation time becomes unsustainable. Several shortcuts have thus been experimented with in recent years, in the form of machine learning algorithms capable of speeding up the process. The evidence seems, however, to suggest that the results of these algorithms are not always reliable. Botond Szabo, Associate Professor at Bocconi Department of Decision Sciences, obtained a €1.5mln ERC Starting Grant for a project aimed at developing mathematical techniques capable of assessing the uncertainty inherent in the estimates derived from machine learning algorithms and, consequently, their reliability in context of statistical models.