iclr-experiments
GitHub用于ICLR论文实验设计与审计，涵盖基线对比、消融实验、扩展律及鲁棒性分析。旨在通过计算匹配控制、多种子方差报告及机制隔离，回应审稿人质疑，确保实证结论的科学性与可复现性。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-experiments -g -y
SKILL.md
Frontmatter
{
"name": "iclr-experiments",
"description": "Use when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting. Use when a reviewer questions whether a representation-learning or model gain is real, when you must isolate one mechanism with an ablation, or when preparing a small compute-matched control that can be posted inline during the public discussion period."
}
ICLR Experiments
Use this before submission or during a revision pass to stress-test empirical claims. ICLR experiments should answer the scientific question, not merely assemble a leaderboard.
Experiment audit
- Match each experiment to a claim in the introduction.
- Compare against current strong baselines, open-source systems, and the most relevant recent OpenReview/arXiv papers.
- Add ablations that isolate one mechanism at a time.
- Report variance across seeds or runs when randomness can change conclusions.
- Include robustness checks for dataset shift, prompt changes, architecture variants, hyperparameter sensitivity, or compute scale when those affect the claim.
- State compute budget, hardware, training time, inference cost, and environmental or access limits where relevant.
- For human evaluation, document task, annotator instructions, aggregation, quality control, and IRB or ethics status when needed.
Reviewer questions to pre-answer
- Is the baseline tuned fairly?
- Does the method win because of more compute, data, parameters, or prompt search?
- Does the effect persist outside the easiest benchmark?
- Are negative results hidden?
- Can a reviewer reproduce the headline table from the supplement or artifact?
What ICLR reviewers reward in evidence
ICLR's empirical culture prizes honest ablations and mechanism over leaderboard position. A clean ablation that explains why a representation works often outscores a larger raw number.
| Claim type | Evidence that convinces ICLR reviewers | Common reject trigger |
|---|---|---|
| New objective helps | Ablate the objective with everything else fixed | Gains confounded with extra tuning |
| Method scales | Several model sizes/tasks with a trend | One large run, no scaling curve |
| Robust representation | Tests across shifts, seeds, prompts | Single-seed peak on one benchmark |
| Beats prior method | Tuned, current, open-source baseline | Stale or under-tuned baseline |
Worked vignette
A paper claims a new self-supervised pretext task yields better linear-probe accuracy. Reviewers ask whether the gain is the pretext task or simply longer pretraining. The author audit: hold total pretraining compute fixed, swap only the pretext objective, and report linear-probe accuracy with error bars over five seeds. The compute-matched ablation isolates the mechanism and is small enough to post inline during discussion, where the table becomes part of the permanent public record.
Reviewer-pushback patterns
- "You win because of more compute." Add a compute-matched control; report FLOPs, not just wall time.
- "Only one seed." Report mean and spread across seeds; an unstable benchmark needs variance.
- "Baseline is weak." Cite the baseline's own recommended settings and show you matched them.
- "Ablation removes two things at once." Split into single-mechanism ablations a reviewer can read.
Output format
[Claim] <paper claim>
[Experiment evidence] sufficient / needs baseline / needs ablation / needs robustness
[Fairness issue] <compute, tuning, data, prompt, metric>
[Fast fix] <experiment or analysis feasible before deadline>
[Appendix placement] <what can move out of main text>
Version History
- 1839142 Current 2026-07-05 13:19
