Speaker
Mikael Kuusela
(Carnegie Mellon University (US))
Description
Many model-independent search methods can be understood as performing a high-dimensional two-sample test. The test is typically performed by training a neural network over the high-dimensional feature space. If the test indicates a significant deviation from the background, it would be desirable to be able to characterize the "signal" the network may have found. In this talk, I will describe our work on interpreting semi-supervised classifier tests using active subspaces to understand the properties of the detected signal. Additionally, I will show how to extract the signal strength parameter from the trained classifier.
