Minimizing Dynamic Regret and Adaptive Regret Simultaneously

AISTATS, pp. 309-319, 2020.

Cited by: 3|Bibtex|Views40
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Abstract:

Regret minimization is treated as the golden rule in the traditional study of online learning. However, regret minimization algorithms tend to converge to the static optimum, thus being suboptimal for changing environments. To address this limitation, new performance measures, including dynamic regret and adaptive regret have been propo...More

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