ProML: Progressive Multi-label Learning
ProML introduces Progressive Multi-Label Learning to solve optimization challenges in multi-label tasks by decoupling label fitting from predictor learning using an auxiliary variable P. This method reshapes the loss landscape, leading to more stable optimization, controllable trade-offs between accuracy and regularization, and better generalization bounds.