The design and analysis of robust Bayesian methods for high dimensional statistical settings is at the cornerstone of modern real world learning problems. They have recently attracted a lot of attention thanks to newly widespread massive computational resources and the keen interest for pivotal applications which require interpretable solutions and uncertainty estimation without which inference procedures provide very few guarantees.
The scientific core of this week is to provide a rich overview of end-to-end Bayesian solutions to real world learning problems in complex settings. The week will highlight all aspects of such approaches from data processing and feature engineering to Monte Carlo estimation procedures as well as the most recent theoretical guarantees obtained in high dimensional settings or frameworks with dependent and/or missing data.
The scientific program of this week includes 2 mini courses, 6 plenary talks, 15 contributed talks and a practical session.
