Derives smoothness-based PAC-Bayes derandomization bounds for deterministic predictors using Rademacher complexity of the Jensen gap class, yielding Jacobian/Hessian flatness terms and a practical regularizer tested on CIFAR-10.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Optimal data difficulty for LLM supervised fine-tuning shifts toward harder examples as data budget increases due to the generalization-extrapolation tradeoff.
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Smoothness-Based Derandomization of PAC-Bayes Bounds
Derives smoothness-based PAC-Bayes derandomization bounds for deterministic predictors using Rademacher complexity of the Jensen gap class, yielding Jacobian/Hessian flatness terms and a practical regularizer tested on CIFAR-10.
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Data Difficulty and the Generalization--Extrapolation Tradeoff in LLM Fine-Tuning
Optimal data difficulty for LLM supervised fine-tuning shifts toward harder examples as data budget increases due to the generalization-extrapolation tradeoff.