Proximal online gradient is optimum for dynamic regret: A general lower bound
Y Zhao, S Qiu, K Li, L Luo, J Yin… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In online learning, the dynamic regret metric chooses the reference oracle 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). While the online gradient (OG) method has been shown to be optimal for
the static regret metric, the optimal algorithm for the dynamic regret remains unknown. In this …
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). While the online gradient (OG) method has been shown to be optimal for
the static regret metric, the optimal algorithm for the dynamic regret remains unknown. In this …
Proximal online gradient is optimum for dynamic regret
Y Zhao, S Qiu, J Liu - arXiv preprint arXiv:1810.03594, 2018 - arxiv.org
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). While the online gradient method has been shown to be optimal
for the static regret metric, the optimal algorithm for the dynamic regret remains unknown. In …
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). While the online gradient method has been shown to be optimal
for the static regret metric, the optimal algorithm for the dynamic regret remains unknown. In …
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