Optimizing Evaluation Metrics for Multi-Task Learning via the Alternating Direction Method of Multipliers

no code implementations • 12 Oct 2022 • Ge-Yang Ke, Yan Pan, Jian Yin, Chang-Qin Huang

The formulation of MTL that directly optimizes evaluation metrics is the combination of two parts: (1) a regularizer defined on the weight matrix over all tasks, in order to capture the relatedness of these tasks; (2) a sum of multiple structured hinge losses, each corresponding to a surrogate of some evaluation metric on one task.

Multi-Task Learning