Learning Uncertainties the Frequentist Way

by Jesse Thaler (MIT)


Uncertainty quantification is a hot topic in machine learning research, but the precise type of "uncertainty" being quantified is not always
clear.  In this talk, I highlight different types of uncertainties that arise in the context of particle physics, and I explain why special care must be taken when using machine learning for frequentist statistics. Focusing on the task of simulation-based calibration, I  then introduce a new machine-learning-based method to quantify the "resolution" of a detector.  Using public collider simulations from the CMS experiment, I demonstrate how this technique can achieve improved jet energy resolutions compared to traditional methods, with minimal additional computational overhead.

Jesse Thaler is a Particle Theorist at MIT, and is the inaugural Director of the Institute for AI and Fundamental Interactions. He has used ML
techniques in many applications to experimental Particle Physics, and has won numerous awards for his work in this field.


Organized by

O. Behnke, L. Lyons, L. Moneta, N. Wardle

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Olaf Behnke
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Nicholas Wardle
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