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New bound clarifies uniform stability in machine learning · arXiv cs.LG

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Researchers have developed a new logarithmic-free upper bound for uniform stability in machine learning algorithms. This bound demonstrates that a $\gamma$-uniformly stable algorithm with a loss in $[0,L]$ has a generalization gap of at most $O(\gamma\log(1/\delta) + L\sqrt{\frac{\log(1/\delta)}{n}})$ with probability $1-\delta$. The study also presents a construction that achieves this optimal dependence on uniform stability, closing a gap in previous research regarding bounded-loss learning algorithms. AI

IMPACT This research refines theoretical understanding of generalization bounds, potentially influencing future algorithm design.

RANK_REASON The cluster contains a research paper published on arXiv concerning theoretical aspects of machine learning.

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New bound clarifies uniform stability in machine learning · arXiv cs.LG

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Pahan Dewasurendra ·

    The Sharp Tail of Uniform Stability

    arXiv:2608.24098v1 Announce Type: new Abstract: Uniform stability controls how much one training example can change the loss at any test point. A new logarithmic-free upper bound shows that a $\gamma$-uniformly stable algorithm with loss in $[0,L]$ has generalization gap at most …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    The Sharp Tail of Uniform Stability

    Uniform stability controls how much one training example can change the loss at any test point. A new logarithmic-free upper bound shows that a $γ$-uniformly stable algorithm with loss in $[0,L]$ has generalization gap at most $O \left(γ\log(1/δ) +L\sqrt{\frac{\log(1/δ)}{n}}\righ…

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