Pith Number

pith:F3CWLV4Y

pith:2026:F3CWLV4YJ2TJ5Z3TPXBZUYLLT7

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SMART Fine-tuning Factor Augmented Neural Lasso

Cheng Gao, Jianqing Fan, Jinhang Chai, Qishuo Yin

Fine-tuning the factor-augmented neural Lasso yields minimax-optimal excess risk bounds and statistical acceleration over single-task learning when relative sample sizes and function complexities align in high-dimensional nonparametric reg

arxiv:2604.12288 v2 · 2026-04-14 · stat.ML · cs.LG · stat.ME

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\pithnumber{F3CWLV4YJ2TJ5Z3TPXBZUYLLT7}

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We derive minimax-optimal excess risk bounds for the fine-tuning FAN-Lasso, characterizing the precise conditions, in terms of relative sample sizes and function complexities, under which fine-tuning yields statistical acceleration over single-task learning.

C2weakest assumption

The target function admits a residual fine-tuning decomposition as a transformation of a frozen source function plus other variables, combined with a low-rank factor structure adequately capturing high-dimensional dependent covariates.

C3one line summary

FAN-Lasso uses low-rank factor structures and a residual fine-tuning decomposition to enable transfer learning and variable selection in high-dimensional nonparametric regression, delivering minimax-optimal excess risk bounds under conditions on sample sizes and function complexity.

Receipt and verification

First computed	2026-05-20T00:04:31.390907Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2ec565d7984ea69ee7737dc39a616b9fd18ca322b2413a69c5cad55ffc6f6ec0

Aliases

arxiv: 2604.12288 · arxiv_version: 2604.12288v2 · doi: 10.48550/arxiv.2604.12288 · pith_short_12: F3CWLV4YJ2TJ · pith_short_16: F3CWLV4YJ2TJ5Z3T · pith_short_8: F3CWLV4Y

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/F3CWLV4YJ2TJ5Z3TPXBZUYLLT7 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2ec565d7984ea69ee7737dc39a616b9fd18ca322b2413a69c5cad55ffc6f6ec0

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b3ddaf0f39198f614c5518bfbd256b1bd8c3718714bdc4834eacd3bb07da6819",
    "cross_cats_sorted": [
      "cs.LG",
      "stat.ME"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ML",
    "submitted_at": "2026-04-14T05:01:18Z",
    "title_canon_sha256": "980a89b06e37953743886640d4c3649515de6f13d2c1c8b0d764dc9022de0ea5"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.12288",
    "kind": "arxiv",
    "version": 2
  }
}