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SUMMARY:Tommaso Dorigo (Italian Institute for Nuclear Physics)
DTSTART:20201125T180000Z
DTEND:20201125T190000Z
DTSTAMP:20260423T003243Z
UID:MPML/19
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/19/">De
 aling with Systematic Uncertainties in HEP Analysis with Machine Learning 
 Methods</a>\nby Tommaso Dorigo (Italian Institute for Nuclear Physics) as 
 part of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbs
 tract\nI will discuss the impact of nuisance parameters on the effectivene
 ss of supervised classification in high energy physics problems\, and tech
 niques 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.\n
LOCATION:https://researchseminars.org/talk/MPML/19/
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