Title: Characterising the Area Under the Curve Loss Function Landscape. Abstract:  One of the most common metrics to evaluate neural network classifiers is the
area under the receiver operating characteristic curve (AUC). However, 
optimisation of the AUC as the loss function during network
training is not a standard procedure. Here we compare minimising the cross-entropy (CE) loss
and optimising the AUC directly. In particular, we analyse the loss function
landscape (LFL) of approximate AUC (appAUC) loss functions to discover
the organisation of this solution space. We discuss various surrogates for AUC approximation and show their differences.
We find that the characteristics of the appAUC landscape are significantly
different from the CE landscape. The approximate AUC loss function improves
testing AUC, and the appAUC landscape has substantially more minima, but
these minima are less robust, with larger average Hessian eigenvalues. We provide a theoretical foundation to explain these results.
To generalise our results, we lastly provide an overview of how the
LFL can help to guide loss function analysis and selection. 