The session focuses on current trends at the intersection of machine learning and classical statistical inference. It covers topics such as model interpretability, uncertainty quantification, and extensions of linear methods for analyzing large and complex data sets. The aim is to highlight how statistical principles contribute to the development of reliable and transparent AI models.
Organized by:
Tomasz Górecki (Poland)Invited Speakers:
- Wojciech Rejchel (Poland)
Positive unlabeled data and prior shift estimation - Łukasz Smaga (Poland)
- Piotr Sulewski (Poland)
Very simple and relatively precise mapping of the normal quantile function intended to fastly generate normal pseudo-random numbers