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• W2982094913 abstract "The online meta-learning framework is designed for the continual lifelonglearning setting. It bridges two fields: meta-learning which tries to extractprior knowledge from existing tasks for fast learning of future tasks, andonline-learning which focuses on the sequential setting in which problems arerevealed one by one. In this paper, we generalize the original framework fromconvex to non-convex setting, and introduce the local regret as the alternativeperformance measure. We then apply this framework to stochastic settings, andshow theoretically that it enjoys a logarithmic local regret, and is robust toany hyperparameter initialization. The empirical test on a real-world taskdemonstrates its superiority compared with traditional methods." @default.
• W2982094913 created "2019-11-01" @default.
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• W2982094913 date "2019-10-22" @default.
• W2982094913 modified "2023-09-27" @default.
• W2982094913 title "Online Meta-Learning on Non-convex Setting." @default.
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