MELO aggregates base predictors and their multi-scale EWLS adaptations using MLpol to achieve oracle inequalities against best fixed and time-varying predictors in non-stationary settings.
Proceedings of the 38th International Conference on Machine Learning , pages =
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Limited-adaptivity slate GLM bandit algorithms achieve the same regret as a fully adaptive algorithm, up to constants, under a diversity assumption.
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Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation
MELO aggregates base predictors and their multi-scale EWLS adaptations using MLpol to achieve oracle inequalities against best fixed and time-varying predictors in non-stationary settings.
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Contextual Slate GLM Bandits with Limited Adaptivity
Limited-adaptivity slate GLM bandit algorithms achieve the same regret as a fully adaptive algorithm, up to constants, under a diversity assumption.