A New First-Order Meta-Learning Algorithm with Convergence Guarantees
<|reference_start|>A New First-Order Meta-Learning Algorithm with Convergence Guarantees: 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.<|reference_end|>
@article{chayti2024a,
    title={A New First-Order Meta-Learning Algorithm with Convergence Guarantees},
    author={El Mahdi Chayti and Martin Jaggi},
    journal={arXiv preprint arXiv:2409.03682},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03682},
    primaryClass={cs.LG math.OC}
}