{
  "annotation_id": "66a030a0-9d05-4326-8536-44febf106344",
  "date_created": "2026-02-17T05:53:20.353000Z",
  "date_modified": "2026-02-17T05:53:20.353000Z",
  "file_hash": "2bca51cda7f699c2da1159fec508e13193279075bc1a7bafbf52eedf0a13aedb",
  "private": false,
  "record": {
    "abstract": "This work investigates binary hypothesis testing between $H_0\\sim P_0$ and $H_1\\sim P_1$ in the finite-sample regime under asymmetric error constraints. By employing the ``reverse\" R\\\u0027enyi divergence, we derive novel non-asymptotic bounds on the Type II error probability which naturally establish a strong converse result. Furthermore, when the Type I error is constrained to decay exponentially with a rate $c$, we show that the Type II error converges to 1 exponentially fast if $c$ exceeds the Kullback-Leibler divergence $D(P_1\\|P_0)$, and vanishes exponentially fast if $c$ is smaller. Finally, we present numerical examples demonstrating that the proposed converse bounds strictly improve upon existing finite-sample results in the literature.",
    "arxiv_id": "2601.09550",
    "authors": [
      "Roberto Bruno",
      "Adrien Vandenbroucque",
      "Amedeo Roberto Esposito"
    ],
    "categories": [
      "cs.IT",
      "math.IT"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "A Finite-Sample Strong Converse for Binary Hypothesis Testing via (Reverse) R\\\u0027enyi Divergence",
    "url": "https://arxiv.org/abs/2601.09550",
    "version": "v2"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "ab1584ed-00cf-4e4b-991c-33cb1fb7cf69",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}