Korba Lab

Research focus

ERC Starting Grant · 2025

Efficient infinite-dimensional optimization over measures

Optimization over probability measures has become a powerful approach for solving complex problems that involve probabilistic modeling. Advanced by the PI and collaborators, it extends finite-dimensional optimization to the infinite-dimensional space of probability measures. This framework provides a princi- pled way to address in particular the task of sampling, that refers to the process of drawing samples from a complex probability distribution, either to approximate it or generate new data. In Bayesian machine learning for instance, we can model uncertainty of the predictions by sampling a model’s parameters. Similarly, in generative modeling, sampling is crucial for producing new data…

From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.