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Machine Learning for high-energy physics and the Higgs ML challenge

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If you have a question about this talk, please contact Damien George .

High Energy Physics provides a challenging data domain with data that is highly structured, but also very noisy. Salimans will present what he has learned analysing such data for the Higgs ML challenge, focusing on methods that are able to effectively search through a high dimensional model space while also achieving good statistical efficiency. In addition, he will discuss the role of the physicist in modelling this type of data, and talk about robustly applying the findings to real (not simulated) HEP data. Melis will describe the winning solution of the Higgs ML challenge, the issues related to the evaluation metric and reliable assessment of model performance. He will take a stab at predicting how to achieve larger improvements. [This is a joint presentation.]

This talk is part of the HEP phenomenology joint Cavendish-DAMTP seminar series.

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