Task Weighting in Meta-learning with Trajectory Optimisation

• URL: http://arxiv.org/abs/2301.01400v1
• Date: Wed, 4 Jan 2023 01:36:09 GMT
• Title: Task Weighting in Meta-learning with Trajectory Optimisation
• Authors: Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
• Abstract summary: We introduce a new principled and fully-automated task-weighting algorithm for meta-learning methods. By considering the weights of tasks within the same mini-batch as an action, we cast the task-weighting meta-learning problem to a trajectory optimisation. We empirically demonstrate that the proposed approach out-performs common hand-engineering weighting methods in two few-shot learning benchmarks.
• Score: 37.32107678838193
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing meta-learning algorithms that are un-biased toward a subset of training tasks often requires hand-designed criteria to weight tasks, potentially resulting in sub-optimal solutions. In this paper, we introduce a new principled and fully-automated task-weighting algorithm for meta-learning methods. By considering the weights of tasks within the same mini-batch as an action, and the meta-parameter of interest as the system state, we cast the task-weighting meta-learning problem to a trajectory optimisation and employ the iterative linear quadratic regulator to determine the optimal action or weights of tasks. We theoretically show that the proposed algorithm converges to an $\epsilon_{0}$-stationary point, and empirically demonstrate that the proposed approach out-performs common hand-engineering weighting methods in two few-shot learning benchmarks.

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