On McDiarmid's Inequality under Dependence via Approximate Tensorization of Entropy
Researchers have published a paper detailing advancements in McDiarmid's inequality, a tool applicable to statistics, learning theory, and theoretical computer science. The work highlights how approximate tensorization of entropy (ATE) implies McDiarmid's inequality and derives a version for non-isotropic Gaussian random vectors. The findings also extend concentration inequalities to strongly log-concave and log-smooth probability measures, improving upon prior results for non-i.i.d. observations. AI