Pith Number

pith:TAVFBZ5N

pith:2025:TAVFBZ5NM6VK2EEMPWCGZLELTC

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Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes

Jingfeng Wu, Licong Lin, Peter L. Bartlett, Ruiqi Zhang

arxiv:2504.04105 v2 · 2025-04-05 · stat.ML · cs.LG

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\usepackage{pith}
\pithnumber{TAVFBZ5NM6VK2EEMPWCGZLELTC}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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.

Cited by

4 papers in Pith

Finite-Sample Performance of Gradient Descent in Logistic Regression with Gaussian Design

Large-Step Training Dynamics of a Two-Factor Linear Transformer Model

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization

Efficient Logistic Regression with Mixture of Sigmoids

Receipt and verification

First computed	2026-07-05T10:50:48.349378Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

982a50e7ad67aaad108c7d846cac8b98b8f25848b8cffac30a55504780e20fd4

Aliases

arxiv: 2504.04105 · arxiv_version: 2504.04105v2 · doi: 10.48550/arxiv.2504.04105 · pith_short_12: TAVFBZ5NM6VK · pith_short_16: TAVFBZ5NM6VK2EEM · pith_short_8: TAVFBZ5N

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TAVFBZ5NM6VK2EEMPWCGZLELTC \
  | 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: 982a50e7ad67aaad108c7d846cac8b98b8f25848b8cffac30a55504780e20fd4

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "36b15aba65800679ee6b8fcaad36c598cb34c6f8546e68ba48d7e835703fe304",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ML",
    "submitted_at": "2025-04-05T08:34:20Z",
    "title_canon_sha256": "28f3e56d5b3e1a6d35992573bf616ceab2862ee7d2ee1fa6ddd03c1447face81"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2504.04105",
    "kind": "arxiv",
    "version": 2
  }
}