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.
Adaptive random forests for evolving data stream classification
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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.
A hybrid active-online learning framework maintains near-ceiling accuracy in optical network failure detection by labeling only 3.4% of streaming samples via margin-based selection.
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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Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection
A hybrid active-online learning framework maintains near-ceiling accuracy in optical network failure detection by labeling only 3.4% of streaming samples via margin-based selection.