  We introduce a new framework for studying meta-learning methods using
PAC-Bayesian theory. Its main advantage over previous work is that it allows
for more flexibility in how the transfer of knowledge between tasks is
realized. For previous approaches, this could only happen indirectly, by means
of learning prior distributions over models. In contrast, the new
generalization bounds that we prove express the process of meta-learning much
more directly as learning the learning algorithm that should be used for future
tasks. The flexibility of our framework makes it suitable to analyze a wide
range of meta-learning mechanisms and even design new mechanisms. Other than
our theoretical contributions we also show empirically that our framework
improves the prediction quality in practical meta-learning mechanisms.
