Meta Learning via Learned LossDownload PDF

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • TL;DR: Meta learning a loss function for optimisation for supervised learning tasks as well as reinforcement learning
  • Abstract: We present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures. We develop a pipeline for training such loss functions, targeted at maximizing the performance of model learn- ing with them. We observe that the loss landscape produced by our learned losses significantly improves upon the original task-specific losses in both supervised and reinforcement learning tasks. Furthermore, we show that our meta-learning framework is flexible enough to incorporate additional information at meta-train time. This information shapes the learned loss function such that the environment does not need to provide this information during meta-test time.
  • Keywords: Meta Learning, Reinforcement Learning, Loss Learning
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