Unverified50% confidenceFactExact time
The study characterizes the maximum range over which coefficients that depend only on multiplicity remain effective for an unrestricted learner, beyond which full frequency-profile information must be introduced.
1
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50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
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Related Claims
UnverifiedOn non-degenerate classes, every consistent learner (no matter how it breaks ties) achieves the optimal realizable sample complexity, with the gap only within a constant factor.78% similarUnverifiedLarger batch sizes typically require higher learning rates (the linear scaling rule).69% similarUnverifiedAfter adopting a frequency-aware training schedule (sampling rare tokens at a rate higher than uniform sampling), the model matches the parameter-matched BERT and TinyBERT baselines on the rarest token-frequency bin.68% similarUnverifiedHigh-dimensional structure learning of Gaussian DAGs faces a statistical-computational gap: methods with sharp sample complexity rely on computationally expensive subset searches and require a pre-specified in-degree upper bound, while polynomial-time algorithms exhibit comparatively inferior sample complexity.68% similarUnverifiedIn the agnostic learning setting, the gap between the sample complexity of every ERM and the known lower bound is at most the ratio of the DS dimension to the Natarajan dimension multiplied by a constant.67% similar
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