The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal

We analyze the Kozachenko–Leonenko (KL) nearest neighbor estimator for the differential entropy. We obtain the first uniform upper bound on its performance over Hölder balls on a torus without assuming any conditions on how close the density could be from zero. Accompanying a new minimax lower bound over the Hölder ball, we show that the KL estimator is achieving the minimax rates up to logarithmic factors without cognizance of the smoothness parameter s of the Hölder ball for s ∈ (0, 2] and arbitrary dimension d, rendering it the first estimator that provably satisfies this property.