  Parametric models, and particularly neural networks, require weight
initialization as a starting point for gradient-based optimization. Recent work
shows that a specific initial parameter set can be learned from a population of
supervised learning tasks. Using this initial parameter set enables a fast
convergence for unseen classes even when only a handful of instances is
available (model-agnostic meta-learning). Currently, methods for learning model
initializations are limited to a population of tasks sharing the same schema,
i.e., the same number, order, type, and semantics of predictor and target
variables. In this paper, we address the problem of meta-learning parameter
initialization across tasks with different schemas, i.e., if the number of
predictors varies across tasks, while they still share some variables. We
propose Chameleon, a model that learns to align different predictor schemas to
a common representation. In experiments on 23 datasets of the OpenML-CC18
benchmark, we show that Chameleon can successfully learn parameter
initializations across tasks with different schemas, presenting, to the best of
our knowledge, the first cross-dataset few-shot classification approach for
unstructured data.
