Distributionally robust minimization in meta-learning for system identification

• URL: http://arxiv.org/abs/2506.18074v1
• Date: Sun, 22 Jun 2025 15:41:22 GMT
• Title: Distributionally robust minimization in meta-learning for system identification
• Authors: Matteo Rufolo, Dario Piga, Marco Forgione,
• Abstract summary: This work explores distributionally robust minimization in meta learning for system identification.<n>We use an alternative approach, adopting a distributionally robust optimization paradigm that prioritizes high-loss tasks, enhancing performance in worst-case scenarios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meta learning aims at learning how to solve tasks, and thus it allows to estimate models that can be quickly adapted to new scenarios. This work explores distributionally robust minimization in meta learning for system identification. Standard meta learning approaches optimize the expected loss, overlooking task variability. We use an alternative approach, adopting a distributionally robust optimization paradigm that prioritizes high-loss tasks, enhancing performance in worst-case scenarios. Evaluated on a meta model trained on a class of synthetic dynamical systems and tested in both in-distribution and out-of-distribution settings, the proposed approach allows to reduce failures in safety-critical applications.

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