Multi-task learning (MTL) has been widely applied in online advertising and
recommender systems. To address the negative transfer issue, recent studies
have proposed optimization methods that thoroughly focus on the gradient
alignment of directions or magnitudes. However, since prior study has proven
that both general and specific knowledge exist in the limited shared capacity,
overemphasizing on gradient alignment may crowd out task-specific knowledge,
and vice versa. In this paper, we propose a transference-driven approach CoGrad
that adaptively maximizes knowledge transference via Coordinated Gradient
modification. We explicitly quantify the transference as loss reduction from
one task to another, and then derive an auxiliary gradient from optimizing it.
We perform the optimization by incorporating this gradient into original task
gradients, making the model automatically maximize inter-task transfer and
minimize individual losses. Thus, CoGrad can harmonize between general and
specific knowledge to boost overall performance. Besides, we introduce an
efficient approximation of the Hessian matrix, making CoGrad computationally
efficient and simple to implement. Both offline and online experiments verify
that CoGrad significantly outperforms previous methods.Comment: 5 pages, 3 figure