Europe/Lisbon — Online

Tommaso Dorigo

Tommaso Dorigo, Italian Institute for Nuclear Physics
Dealing with Systematic Uncertainties in HEP Analysis with Machine Learning Methods

I will discuss the impact of nuisance parameters on the effectiveness of supervised classification in high energy physics problems, and techniques that may mitigate or remove their effect in the search for optimal selection criteria and variable transformations. The approaches discussed include nuisance parametrized models, modified or adversary losses, semi supervised learning approaches and inference-aware techniques.


Additional file

Dorigo slides.pdf