  We present SOSELETO (SOurce SELEction for Target Optimization), a new method
for exploiting a source dataset to solve a classification problem on a target
dataset. SOSELETO is based on the following simple intuition: some source
examples are more informative than others for the target problem. To capture
this intuition, source samples are each given weights; these weights are solved
for jointly with the source and target classification problems via a bilevel
optimization scheme. The target therefore gets to choose the source samples
which are most informative for its own classification task. Furthermore, the
bilevel nature of the optimization acts as a kind of regularization on the
target, mitigating overfitting. SOSELETO may be applied to both classic
transfer learning, as well as the problem of training on datasets with noisy
labels; we show state of the art results on both of these problems.
