icml-related-work

GitHub

针对ICML投稿,分析新颖性与增量贡献风险。覆盖邻近领域文献、并发提交处理及匿名引用规范。提供差异化段落模板与应对审稿人质疑的策略表,输出包含竞品对比、风险评估及技术差异的标准化报告。

ICML-Skills/skills/icml-related-work/SKILL.md brycewang-stanford/Awesome-Journal-Skills

Trigger Scenarios

评估ICML论文的新颖性 处理并发提交或重叠作者工作 识别增量贡献风险 准备相关工作部分的差异化陈述

Install

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-related-work -g -y
More Options

Non-standard path

npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICML-Skills/skills/icml-related-work -g -y

Use without installing

npx skills use brycewang-stanford/Awesome-Journal-Skills@icml-related-work

指定 Agent (Claude Code)

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-related-work -a claude-code -g -y

安装 repo 全部 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --all -g -y

预览 repo 内 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --list

SKILL.md

Frontmatter
{
    "name": "icml-related-work",
    "description": "Use when positioning an ICML submission against close ML literature, concurrent ICML submissions, recent public papers, workshop papers, ICLR\/AISTATS\/NeurIPS neighbors, and prior work under double-blind constraints."
}

ICML Related Work

Use this when novelty, incremental contribution, or concurrent-submission handling is the risk. ICML 2026 treats related concurrent ICML submissions with overlapping authors as prior work.

Required coverage

  • Closest ML methods, theory, datasets, benchmarks, and evaluation papers.
  • Neighboring venue papers from NeurIPS, ICLR, AISTATS, UAI, COLT, MLSys, KDD, ACL, CVPR, and other relevant areas.
  • Related concurrent ICML submissions by overlapping authors; cite anonymously and include PDFs in supplementary material when a reasonable reviewer would expect them.
  • Workshop papers without published proceedings generally do not trigger dual-submission violation, but their relationship still needs honest positioning.
  • Very recent public work close to the full-paper deadline can be treated as concurrent, but good judgment and subfield norms matter.

Delta paragraph

<Prior work> addresses <problem> using <mechanism>. It leaves <specific gap>.
Our submission differs by <technical delta>, and this matters because <evidence>.

Novelty-pushback table (ICML reviewer reflexes)

ICML reviewers triage novelty fast because the load is heavy and the first PMLR page must already signal the delta. Map the likely objection to a venue-specific repair.

Reviewer objection What it means at ICML Repair before submission
"This is a known trick rebranded" Mechanism overlaps a COLT/NeurIPS result Cite it, state the assumption or rate that differs, move the delta to page 1
"Concurrent work already does this" Overlapping-author ICML paper or recent arXiv Cite anonymously, attach the PDF, give a one-line technical separation
"Optimization angle is incremental" A new step-size or proximal variant Show the regime where prior rates fail and yours holds, plus a tuned-baseline plot
"Benchmark-only contribution" No mechanism, just leaderboard Add an ablation tying the gain to the proposed component

Worked vignette: a new optimizer with convergence theory

Suppose the paper proposes an adaptive-step method with a non-convex convergence guarantee plus deep-learning benchmarks. The related-work risk is that reviewers know Adam, AdaGrad, Lion, and the escaping-saddle and variance-reduction literature. Position it by separating the assumption set (smoothness, bounded variance, PL condition) your rate needs from what neighbors assume, naming the closest COLT/NeurIPS optimization theory, and conceding which benchmarks are shared rather than claiming a blanket win. The delta paragraph then reads as a precise rate-and-regime statement, not a list of beaten methods.

Output format

[Closest work] <3-5 papers or clusters>
[Concurrent ICML handling] none / cite anonymously / include PDF / split papers
[Incrementality risk] high / medium / low
[Technical delta] <one sentence>
[Related-work rewrite] <paragraph>

Version History

  • 1839142 Current 2026-07-05 13:19

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Metadata

Files
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Version
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Hash
03f1c94a
Indexed
2026-07-05 13:19

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