New theory tightens bounds on list replicability in PAC learning

Researchers have established a new upper bound for list replicability in the PAC learning framework, significantly improving upon previous results. The study demonstrates that for concept classes with a Littlestone dimension of 'd' and a dual VC dimension of 'v*', list replicability is bounded by $2^{O(d \min\{\log d, v^*\})}$. This new bound is nearly optimal, closely matching the known lower bound of $2^{\Omega(d)}$ and exponentially improving the prior best upper bound of $2^{2^{O(d)}}$. The findings are derived from a novel inductive framework and leverage tools from differentially private learning and stable regularity. AI

IMPACT Establishes new theoretical bounds relevant to machine learning algorithm design and analysis.

RANK_REASON This is a theoretical computer science paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• ACM Symposium on Theory of Computing
• arXivLabs
• Ghazi
• Golowich
• Kumar
• Littlestone dimension
• Livni
• Manurangsi
• Moran
• probably approximately correct learning
• VC dimension

• paper
• other

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