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.
Incremental learning of concept drift from streaming imbalanced data
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A latent-cluster quasi-Bayesian method with restarted updates yields sublinear cumulative Wasserstein-1 regret for online distributional prediction under drift and adversarial corruption.
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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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Online Distributional Prediction via Latent Cluster Geometry Under Drift and Corruption
A latent-cluster quasi-Bayesian method with restarted updates yields sublinear cumulative Wasserstein-1 regret for online distributional prediction under drift and adversarial corruption.