A dynamic pruning reduction from agnostic to realizable online learning via weak-consistency oracles achieves O(T^{d_VC+1}) query complexity with near-optimal regret and supplies matching upper and lower bounds on the regret-oracle tradeoff.
Proceedings of The 27th Conference on Learning Theory , pages =
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In multi-label neural collapse, terminal geometry is controlled by the centered label covariance spectrum κ_m derived from label distribution moments, with higher-multiplicity prototypes following class-frequency-weighted synthesis instead of uniform averaging.
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Regret-Oracle Complexity Tradeoffs in Agnostic Online Learning
A dynamic pruning reduction from agnostic to realizable online learning via weak-consistency oracles achieves O(T^{d_VC+1}) query complexity with near-optimal regret and supplies matching upper and lower bounds on the regret-oracle tradeoff.
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How Label Imbalance Shapes Geometry: A General Spectral Analysis of Multi-Label Neural Collapse
In multi-label neural collapse, terminal geometry is controlled by the centered label covariance spectrum κ_m derived from label distribution moments, with higher-multiplicity prototypes following class-frequency-weighted synthesis instead of uniform averaging.