Theory Seminars WS 19/20

 

Thu 10.10.2019, 16.30 h

J. Thompson (Uni Heidelberg)

(Machine) Learning to be Unsure

Abstract:
Machine learning methods are becoming ubiquitous in particle physics. However, many of these techniques
suffer from overconfidence and a lack of associated uncertainties. In this talk, I will discuss how Bayesian neural networks can address these problems and help us to understand model uncertainties in a machine learning context. In order to do this, I will first explore how these uncertainties can be understood in a frequentist framework, before considering how the Bayesian neural network responds to statistical and systematic uncertainties.

 

Thu 17.10.2019, 16.30 h

S. Paßehr (RWTH Aachen)

Towards high-precision predictions for Higgs masses and decays in theories beyond the
Standard Model

 

Thu 24.10.2019, 16.30 h

K. Ng (GRAPPA, Amsterdam)

TBA

 

Thu 31.10.2019, 16.30 h

M. Mühlleitner (KIT)

TBA

 

Thu 07.11.2019, 16.30 h

R. Teyssier (Uni Zürich)

TBA

 

Thu 14.11.2019, 16.30 h

L. Magnea (U. Torino)

TBA

 

Thu 21.11.2019, 16.30 h

M. Bustamante (NBI, Copenhagen)

TBA

 

Thu 05.12.2019, 16.30 h

D.J.E. Marsh (Uni Göttingen)

TBA

 

Thu 12.12.2019, 16.30 h

M. Wiesemann (CERN und MPI München)

TBA

 

Thu 09.01.2020, 16.30 h

A. Fialkov (University of Sussex)

TBA

 

Thu 16.01.2020, 16.30 h

M. Diehl (DESY)

TBA

 

Thu 23.01.2020, 16.30 h

A. Font-Ribera (UC London)

TBA

 

Thu 30.01.2020, 16.30 h

C. Sturm (U. Würzburg)

TBA