{
  "annotation_id": "efde5413-1472-441a-995f-d40f6af06eef",
  "date_created": "2026-02-17T05:53:23.478000Z",
  "date_modified": "2026-02-17T05:53:23.478000Z",
  "file_hash": "a673ef3f2d1226561648458031f2bc745ded41275e2287347e4e024b4fe0e9ff",
  "private": false,
  "record": {
    "abstract": "In this work, we provide a new convergence theory for plug-and-play proximal gradient descent (PnP-PGD) under prior mismatch where the denoiser is trained on a different data distribution to the inference task at hand. To the best of our knowledge, this is the first convergence proof of PnP-PGD under prior mismatch. Compared with the existing theoretical results for PnP algorithms, our new results removed the need for several restrictive and unverifiable assumptions.",
    "arxiv_id": "2601.09831",
    "authors": [
      "Guixian Xu",
      "Jinglai Li",
      "Junqi Tang"
    ],
    "categories": [
      "cs.LG",
      "math.OC"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "A New Convergence Analysis of Plug-and-Play Proximal Gradient Descent Under Prior Mismatch",
    "url": "https://arxiv.org/abs/2601.09831",
    "version": "v2"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "ae31d432-5484-4a55-9fd0-15ba5db87695",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}