Part of Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Main Conference Track
Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado P. van Hasselt, András György, Satinder Singh
We study the connection between gradient-based meta-learning and convex optimisation. We observe that gradient descent with momentum is a special case of meta-gradients, and building on recent results in optimisation, we prove convergence rates for meta learning in the single task setting. While a meta-learned update rule can yield faster convergence up to constant factor, it is not sufficient for acceleration. Instead, some form of optimism is required. We show that optimism in meta-learning can be captured through the recently proposed Bootstrapped Meta-Gradient (Flennerhag et. al., 2022) method, providing deeper insight into its underlying mechanics.