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arxiv: 2603.10190 · v2 · pith:FCH2EHSFnew · submitted 2026-03-10 · 🧮 math.OC · math.PR

Hoeffding-Style Concentration Bounds for Exchangeable Random Variables

classification 🧮 math.OC math.PR
keywords concentrationexchangeablemeansrandomboundboundscentereddistributions
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We establish Hoeffding-type concentration inequalities for the lower and upper tails of finite sums of exchangeable random variable sequences. In contrast to the existing literature, our concentration bounds are expressed in terms of the largest and smallest means among the distributions in the support of the de Finetti mixing measure, rather than the population mean. Specifically, the upper-tail bound is centered at the largest such mean, while the lower-tail bound is centered at the smallest. These results bridge the gap between finite-sample and population means of exchangeable random variables, and the means of the underlying distributions in the de Finetti representation.

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  1. Bounded Difference Concentration for Infinitely Exchangeable Sequences with Applications to AI Benchmark Uncertainty

    stat.ML 2026-06 unverdicted novelty 6.0

    Establishes concentration bounds for infinitely exchangeable sequences with cancellation for zero-sum contrasts and applies the result to distribution-free uncertainty quantification in composite AI benchmarks.