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math.ST2026
A One-Sided McDiarmid-Type Inequality for Heavy Tails via Reverse KL Tensorization
Hanuka Eshima, Amedeo Roberto Esposito, Andrea Paudice
We revisit McDiarmid's inequality, a central tool in statistical learning theory and algorithm analysis. We show that large deviations above the mean are exponentially unlikely for…
math.ST2026
On the Impossibility of Estimating the Average Mixing Time
Nathan A. Judd, Amedeo Roberto Esposito
We establish nearly-matching converse bounds on the sample complexity of estimating the average mixing time of a Markov chain from a single stationary trajectory. Unlike the worst-…