Proximal Online Gradient is Optimum for Dynamic Regret

8 Oct 2018 Yawei Zhao Shuang Qiu Ji Liu

In online learning, the dynamic regret metric chooses the reference (optimal) solution that may change over time, while the typical (static) regret metric assumes the reference solution to be constant over the whole time horizon. The dynamic regret metric is particularly interesting for applications such as online recommendation (since the customers' preference always evolves over time)... (read more)

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