Description
Date depot: 1 janvier 1900
Titre: Scalable Inference for Gaussian Processes
Directeur de thèse:
Bernard MERIALDO (Eurecom)
Directeur de thèse:
Maurizio FILIPPONE (Eurecom)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini
Resumé:
In many areas of sciences involving the studying of complex phenomena, a fundamental scientific question is how to reliably quantify the level of confidence in any predictions and in any conclusions made on the functioning of systems of interest. A well calibrated quantification of the level of uncertainty in the analysis of these systems makes it possible to reliably assess the level of risk associated with some configurations of the system and balance the cost of decisions. This project employs the expressive language of probabilities to characterize the degree of belief about systems’ variables, as probability theory is the pillar onto which a principled theory for decision-making is built. Applications that motivate the importance of being able to reliably quantify uncertainty include. Systemic diseases – Systemic diseases, such as diabetes, obesity, cardiovascular diseases, Alzeimer's disease, colorectal cancer, hepatocellular carcinoma (HCC), breast cancer, melanoma to name a few, represent some of the main causes of motor impairment and death in the western countries at present. Even though considerable steps have been made in the direction of understanding risk factors associated with these, we are still far from knowing definitive markers that can be targeted to stop or reverse the progression of systemic diseases. Natural disasters – Accurately and timely predicting the occurrence of extreme weather conditions, the effect of tsunamis on coastal reefs, and the incidence and magnitude of earthquakes is important in order to raise alerts in cases when there is a concrete danger for the population. Even though short-term predictions can reasonably be made, the extreme complexity and stochasticity of these systems poses formidable computational challenges in producing accurate predictions and well calibrated confidence levels, so that natural disasters, as of today, still account for several deaths and billions of dollars of damages to cities every year. In many domains, models offer mathematical abstractions encoding any understanding on the way systems evolve and respond to certain inputs. Models are usually characterized by a number of parameters that may have an interpretation as unobserved physical quantities. In many fields, such as climatology and geo-sciences, given the complexity of the phenomena under investigation, there are several competing models that are adopted to make predictions and they are usually computationally intensive to run.
Doctorant.e: Cutajar Kurt