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arXiv · 1910.10196

No-regret Non-convex Online Meta-Learning

Zhenxun Zhuang·Yunlong Wang·Kezi Yu·Songtao Lu
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Abstract

The online meta-learning framework is designed for the continual lifelong learning setting. It bridges two fields: meta-learning which tries to extract prior knowledge from past tasks for fast learning of future tasks, and online-learning which deals with the sequential setting where problems are revealed one by one. In this paper, we generalize the original framework from convex to non-convex setting, and introduce the local regret as the alternative performance measure. We then apply this framework to stochastic settings, and show theoretically that it enjoys a logarithmic local regret, and is robust to any hyperparameter initialization. The empirical test on a real-world task demonstrates its superiority compared with traditional methods.

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Zhenxun Zhuang, Yunlong Wang, Kezi Yu, Songtao Lu. 2019-10-22. No-regret Non-convex Online Meta-Learning. https://arxiv.org/abs/1910.10196

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