Multi-Objective Meta Learning

• URL: http://arxiv.org/abs/2102.07121v1
• Date: Sun, 14 Feb 2021 10:23:09 GMT
• Title: Multi-Objective Meta Learning
• Authors: Feiyang Ye, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, Yu Zhang
• Abstract summary: We propose a unified gradient-based Multi-Objective Meta Learning (MOML) framework. We show the effectiveness of the proposed MOML framework in several meta learning problems.
• Score: 2.9932638148627104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meta learning with multiple objectives can be formulated as a Multi-Objective Bi-Level optimization Problem (MOBLP) where the upper-level subproblem is to solve several possible conflicting targets for the meta learner. However, existing studies either apply an inefficient evolutionary algorithm or linearly combine multiple objectives as a single-objective problem with the need to tune combination weights. In this paper, we propose a unified gradient-based Multi-Objective Meta Learning (MOML) framework and devise the first gradient-based optimization algorithm to solve the MOBLP by alternatively solving the lower-level and upper-level subproblems via the gradient descent method and the gradient-based multi-objective optimization method, respectively. Theoretically, we prove the convergence properties of the proposed gradient-based optimization algorithm. Empirically, we show the effectiveness of the proposed MOML framework in several meta learning problems, including few-shot learning, neural architecture search, domain adaptation, and multi-task learning.

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