Title: SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels. Abstract: 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.