A new optimistic online mirror descent variant uses a post-hoc penalty to allow learning rates up to Θ(T) while bounding cumulative penalty at O(log T), achieving near-optimal dynamic regret and faster adaptation on non-stationary data.
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A cluster-induced distribution shift simulation framework is proposed and used to evaluate six batch adaptation strategies including cluster-local ADWIN on five benchmark datasets.
The study theoretically examines concept drift and evaluates drift detection algorithms across categories on diverse streaming datasets.
citing papers explorer
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Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates
A new optimistic online mirror descent variant uses a post-hoc penalty to allow learning rates up to Θ(T) while bounding cumulative penalty at O(log T), achieving near-optimal dynamic regret and faster adaptation on non-stationary data.
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Cluster-Specific Localized Drift Detection for Efficient Batch Model Adaptation under Controlled Distribution Shift
A cluster-induced distribution shift simulation framework is proposed and used to evaluate six batch adaptation strategies including cluster-local ADWIN on five benchmark datasets.
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Learner-based Concept Drift Detection: Analysis and Evaluation
The study theoretically examines concept drift and evaluates drift detection algorithms across categories on diverse streaming datasets.