A New First-Order Meta-Learning Algorithm with Convergence Guarantees

• URL: http://arxiv.org/abs/2409.03682v1
• Date: Thu, 5 Sep 2024 16:37:26 GMT
• Title: A New First-Order Meta-Learning Algorithm with Convergence Guarantees
• Authors: El Mahdi Chayti, Martin Jaggi,
• Abstract summary: Gradient-based meta-learning, especially MAML, has emerged as a viable solution to accomplish this goal. One problem MAML encounters is its computational and memory burdens needed to compute the meta-gradients. We propose a new first-order variant of MAML that we prove converges to a stationary point of the MAML objective, unlike other first-order variants.
• Score: 37.85411810113886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning new tasks by drawing on prior experience gathered from other (related) tasks is a core property of any intelligent system. Gradient-based meta-learning, especially MAML and its variants, has emerged as a viable solution to accomplish this goal. One problem MAML encounters is its computational and memory burdens needed to compute the meta-gradients. We propose a new first-order variant of MAML that we prove converges to a stationary point of the MAML objective, unlike other first-order variants. We also show that the MAML objective does not satisfy the smoothness assumption assumed in previous works; we show instead that its smoothness constant grows with the norm of the meta-gradient, which theoretically suggests the use of normalized or clipped-gradient methods compared to the plain gradient method used in previous works. We validate our theory on a synthetic experiment.

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