  In (Franceschi et al., 2018) we proposed a unified mathematical framework,
grounded on bilevel programming, that encompasses gradient-based hyperparameter
optimization and meta-learning. We formulated an approximate version of the
problem where the inner objective is solved iteratively, and gave sufficient
conditions ensuring convergence to the exact problem. In this work we show how
to optimize learning rates, automatically weight the loss of single examples
and learn hyper-representations with Far-HO, a software package based on the
popular deep learning framework TensorFlow that allows to seamlessly tackle
both HO and ML problems.
