Speaker
Description
With great classification power comes great responsibility: Now that deep-learning is the de-facto standard for jet classification in high-energy physics, attention needs to be paid to aspects beyond performance. A key issue is the question of decorrelation - how a classifier output can be made independent of other salient variables such as the jet's mass. Achieving reliable decorrelation is crucial for stabilising the network response against systematic uncertainties and for building robust analysis strategies in background rich environments. So far, the most powerful decorrelation approaches are based on adversarial training: two networks performing competing tasks. These are notoriously difficult to train, as the two networks must be carefully tuned against one another, and their objective is unbounded from below. We show how a positive regulariser term based on the distance correlation metric can achieve state-of-the-art decorrelation performance with much simpler training.
