Online-Within-Online Meta-Learning

NeurIPS 2019 Giulia DeneviDimitris StamosCarlo CilibertoMassimiliano Pontil

We study the problem of learning a series of tasks in a fully online Meta-Learning setting. The goal is to exploit similarities among the tasks to incrementally adapt an inner online algorithm in order to incur a low averaged cumulative error over the tasks... (read more)

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