Learning with Symmetric Label Noise: The Importance of Being Unhinged
Convex potential minimisation is the de facto approach to binary classification. However, Long and Servedio  proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing. This ostensibly shows that convex losses are not SLN-robust. In this paper, we propose a convex, classification-calibrated loss and prove that it is SLN-robust. The loss avoids the Long and Servedio...[Show more]
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|Source:||Reflection, Refraction and Hamiltonian Monte Carlo|
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