{
  "annotation_id": "d963dd26-891f-43e1-8eda-6e05d8acc75f",
  "date_created": "2026-02-17T05:53:08.808000Z",
  "date_modified": "2026-02-17T05:53:08.808000Z",
  "file_hash": "47d8255fb677bce65c9b3eea12c12045280f38595571cea4b84fe30926e68c96",
  "private": false,
  "record": {
    "abstract": "We consider the problem of mean estimation under quantization and adversarial corruption. We construct multivariate robust estimators that are optimal up to logarithmic factors in two different settings. The first is a one-bit setting, where each bit depends only on a single sample, and the second is a partial quantization setting, in which the estimator may use a small fraction of unquantized data.",
    "arxiv_id": "2601.07074",
    "authors": [
      "Pedro Abdalla",
      "Junren Chen"
    ],
    "categories": [
      "stat.ML",
      "cs.LG",
      "math.ST",
      "stat.TH"
    ],
    "license": "http://creativecommons.org/publicdomain/zero/1.0/",
    "title": "Robust Mean Estimation under Quantization",
    "url": "https://arxiv.org/abs/2601.07074",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "13497e18-66a9-4fc1-b363-1191f806b761",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}