icdm-reproducibility

GitHub

指导ICDM论文在10页限制及三盲评审下提升可复现性。涵盖配置管理、匿名化代码包构建(去除Git历史等身份信息)、复现等级划分,以及如何在有限篇幅内规范报告种子、超参数和计算资源信息。

ICDM-Skills/skills/icdm-reproducibility/SKILL.md brycewang-stanford/Awesome-Journal-Skills

Trigger Scenarios

准备提交ICDM会议论文 需要生成符合三盲要求的匿名化复现代码包 优化论文中的可复现性章节以节省版面

Install

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdm-reproducibility -g -y
More Options

Non-standard path

npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICDM-Skills/skills/icdm-reproducibility -g -y

Use without installing

npx skills use brycewang-stanford/Awesome-Journal-Skills@icdm-reproducibility

指定 Agent (Claude Code)

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdm-reproducibility -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": "icdm-reproducibility",
    "description": "Use when strengthening reproducibility for an ICDM (IEEE International Conference on Data Mining) paper - seeds, configs, compute and data reporting, and an anonymized reproduction package that survives the Research Track's triple-blind regime while every detail fights for room inside the single 10-page IEEE all-inclusive cap."
}

ICDM Reproducibility

Make the mining result reproducible as science, under two ICDM-specific pressures: the Research Track is triple-blind, so the reproduction package cannot reveal identity, and the reporting all competes for space inside the 10-page all-inclusive cap. Reproducibility at ICDM is not a separate checklist form (verify per edition); it is evidence that the discovery is real rather than a lucky configuration.

What must be reproducible

  • The mining task setup: exact datasets, versions, preprocessing, splits, and how ground truth or injections were generated.
  • The method: hyperparameters, model selection procedure, the mechanism's knobs (e.g. sketch size, partition count), and the random seeds.
  • The evaluation: metrics, thresholds/cutoffs, variance estimation, and the hardware behind any timing claim.

Reproducibility tiers

Tier What a reviewer can do How to reach it
Rerunnable Re-execute your scripts and get your tables Pinned deps, seeds, config files, entry script
Rebuildable Reconstruct the pipeline from description alone Precise protocol in the paper + appendix
Attested Trust numbers that cannot be shared (private data) Documented protocol + synthetic proxy + honest scope

Aim for rerunnable on any public-data result; use attested only where data genuinely cannot be released, and say so plainly.

Configs as artifacts

Put the run behind a config, not scattered command-line flags, so a reviewer reproduces a table by pointing at a file.

# repro/config.yaml  (anonymized; no author paths, no institutional dataset names)
task: stream_anomaly_ranking
dataset: public_edge_stream_v3      # public source + version, not an internal name
seeds: [0, 1, 2, 3, 4, ... , 19]    # the 20 seeds behind the reported variance
method:
  sketch_partitions: 128            # the mechanism knob mapped in the ablation
  hash_family: multiplicative
eval:
  metric: precision_at_k
  k: 100
  time_respecting_split: true
compute:
  hardware: "1x consumer GPU, 16GB"  # generic; states the basis of any timing claim

Triple-blind the package

  • Export a fresh repository with no git history — commit metadata routinely leaks author names and institutions.
  • Remove author paths, usernames, internal dataset names, cluster hostnames, and README acknowledgements.
  • Host it so the link in the PDF resolves to an anonymized location, not a named account.
  • Because ICDM traditionally offers no rebuttal, this package is often the only extra evidence a reviewer ever sees — it must be complete and anonymous at submission time.

Report inside the page cap

  • The full reproduction protocol can live in an in-cap appendix and the cited repository; the body must still state seeds, key hyperparameters, and the compute basis of timing claims.
  • Prefer a compact reproducibility paragraph plus a repository over a sprawling appendix that eats pages the body needs.

Vignette: the seed table that answered a review before it was written

A team worried reviewers would read their close margins as noise. Rather than hope for a rebuttal that ICDM might not offer, they reported every metric with a standard deviation over 20 seeds, shipped the seed list and configs in an anonymized, history-scrubbed repository cited in the PDF, and stated the exact GPU behind their latency numbers. The "is this just noise?" review never came, because the paper had already answered it — and nothing in the package revealed who they were.

Output format

[Repro tier] rerunnable / rebuildable / attested (per result)
[Seeds+variance] reported: yes / no
[Config-as-artifact] present: yes / no
[Anonymized package] history-scrubbed + no institutional names: yes / leaks found
[Compute basis] hardware behind timing claims stated: yes / no
[Top gap] <single most important missing reproducibility detail>

Version History

  • 9f86f09 Current 2026-07-19 15:55

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