Pith Number

pith:ED6BWNEU

pith:2026:ED6BWNEUR7IWZ35DG4GFL3VYNQ

not attested not anchored not stored refs resolved

Adaptive Kernel Density Estimation with Pre-training

Ke Deng, Ruitong Zhang

A pre-trained neural network can recommend location-adaptive kernels to achieve efficient density estimation in high dimensions.

arxiv:2605.13092 v1 · 2026-05-13 · stat.ML · cs.LG · stat.ME

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3 Author claim open · sign in to claim

4 Citations open

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Claims

C1strongest claim

By establishing a pre-trained neural network that can recommend an appropriate location-adaptive kernel for each sample point, efficient density estimation with adaptive kernels is achieved in high dimensions.

C2weakest assumption

The target distribution is sufficiently close to the pre-training distribution family (or can be made so via fine-tuning) and that the neural network reliably recommends kernels that improve estimation accuracy.

C3one line summary

A pre-trained neural network selects adaptive kernels per sample point to enable accurate high-dimensional kernel density estimation.

References

35 extracted · 35 resolved · 0 Pith anchors

[1] 1994 , edition = 1994

[2] 2016 , publisher = 2016

[3] The Annals of Mathematical Statistics , year =

[4] The Annals of Mathematical Statistics , year =

[5] 1990 , series = 1990

Receipt and verification

First computed	2026-05-18T03:08:58.436581Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

20fc1b34948fd16cefa3370c55eeb86c1b4db3592265ae614f477411dbf9047b

Aliases

arxiv: 2605.13092 · arxiv_version: 2605.13092v1 · doi: 10.48550/arxiv.2605.13092 · pith_short_12: ED6BWNEUR7IW · pith_short_16: ED6BWNEUR7IWZ35D · pith_short_8: ED6BWNEU

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ED6BWNEUR7IWZ35DG4GFL3VYNQ \
  | 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: 20fc1b34948fd16cefa3370c55eeb86c1b4db3592265ae614f477411dbf9047b

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b80f0ee336aa4c330316e2658fdfa2ce4c959363ae60597ec3ce6a7bd231a657",
    "cross_cats_sorted": [
      "cs.LG",
      "stat.ME"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ML",
    "submitted_at": "2026-05-13T07:03:54Z",
    "title_canon_sha256": "2a5aa914e5da14cde29ce3700a800c9cd725f8f29d75e9ffded082504060d49f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13092",
    "kind": "arxiv",
    "version": 1
  }
}