DGS-MAML layers gradient matching onto SharpMAML and claims O(1/T) convergence and tighter PAC-Bayes bounds, but the displayed theorems give O(1/sqrt T) under the paper's own parameter choices.
Advances in Neural Information Processing Systems31(2018)
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Domain-Generalization to Improve Learning in Meta-Learning Algorithms
DGS-MAML layers gradient matching onto SharpMAML and claims O(1/T) convergence and tighter PAC-Bayes bounds, but the displayed theorems give O(1/sqrt T) under the paper's own parameter choices.