Auxiliary Learning as an Asymmetric Bargaining Game

Understand

Auxiliary learning is an effective method for enhancing the generalization capabilities of trained models, particularly when dealing with small datasets.

• However, this approach may present several difficulties: (i) optimizing multiple objectives can be more challenging, and (ii) how to balance the auxiliary tasks to best assist the main task is unclear.
• In this work, we propose a novel approach, named AuxiNash, for balancing tasks in auxiliary learning by formalizing the problem as generalized bargaining game with asymmetric task bargaining power.
• Furthermore, we describe an efficient procedure for learning the bargaining power of tasks based on their contribution to the performance of the main task and derive theoretical guarantees for its convergence.