The goal of optimization-based meta-learning is to find a single
initialization shared across a distribution of tasks to speed up the process of
learning new tasks. Conditional meta-learning seeks task-specific
initialization to better capture complex task distributions and improve
performance. However, many existing conditional methods are difficult to
generalize and lack theoretical guarantees. In this work, we propose a new
perspective on conditional meta-learning via structured prediction. We derive
task-adaptive structured meta-learning (TASML), a principled framework that
yields task-specific objective functions by weighing meta-training data on
target tasks. Our non-parametric approach is model-agnostic and can be combined
with existing meta-learning methods to achieve conditioning. Empirically, we
show that TASML improves the performance of existing meta-learning models, and
outperforms the state-of-the-art on benchmark datasets.