On the Comparison of Optimizers for Imbalanced Learning

Cl{é}ment Lezane, Fran{\c c}ois Bachoc, J{é}r{ô}me Bolte +1

Data imbalance is pervasive in machine learning, from rare words and anomalies to underrepresented patterns in heterogeneous or cross-tabulated data. We study idealized optimizers…