  Standard meta-learning for representation learning aims to find a common
representation to be shared across multiple tasks. The effectiveness of these
methods is often limited when the nuances of the tasks' distribution cannot be
captured by a single representation. In this work we overcome this issue by
inferring a conditioning function, mapping the tasks' side information (such as
the tasks' training dataset itself) into a representation tailored to the task
at hand. We study environments in which our conditional strategy outperforms
standard meta-learning, such as those in which tasks can be organized in
separate clusters according to the representation they share. We then propose a
meta-algorithm capable of leveraging this advantage in practice. In the
unconditional setting, our method yields a new estimator enjoying faster
learning rates and requiring less hyper-parameters to tune than current
state-of-the-art methods. Our results are supported by preliminary experiments.
