We propose two novel loss functions based on Jensen-Shannon divergence for learning under label noise. Following the work of Ghosh et al. (2017), we argue about their theoretical robustness. Furthermore, we reveal several other desirable properties by drawing informative connections to various loss functions, e.g., cross entropy, mean absolute error, general