---
name: icml-writing-style
description: Use when rewriting a machine-learning paper for ICML-style claims, 8-page clarity, soundness/originality/significance framing, impact statement, lay-summary readiness, and reviewer-updateable rebuttal posture.
---

# ICML Writing Style

ICML writing has to make a rigorous ML contribution clear inside a tight 8-page body. The style is
less about broad excitement and more about soundness, originality, significance, clarity, and
reproducibility.

## Introduction shape

1. Define the ML problem precisely.
2. State what prior methods, theory, or evaluations fail to cover.
3. Give the contribution in a form a reviewer can evaluate: method, theorem, benchmark, empirical
   finding, system, or analysis.
4. Preview the evidence: proof, ablation, baseline, dataset, or reproducibility artifact.
5. Bound the claim and point to appendices only for support, not for the main idea.

## Style rules

- Put critical evidence in the main body, not only in appendices or supplement.
- Use claims that can be mapped to soundness, originality, significance, and clarity.
- Name assumptions and constraints early.
- Keep the impact statement factual and proportionate.
- Avoid prompt-injection text, identity hints, and non-anonymous repository references.
- Write with the knowledge that original submissions and rebuttal may become public if accepted.

## PMLR two-column discipline

ICML proceedings appear in PMLR's two-column format, so prose must survive narrow columns: short
sentences, equations that do not overflow, and figures legible at column width. Front-load the
contribution because the heavy reviewer load means the first column of page one carries
disproportionate weight; a buried thesis reads as a weak thesis. Check the current style file rather
than assuming last year's margins.

## Claim calibration table

| Overclaim pattern | ICML reviewer reaction | Calibrated rewrite |
| --- | --- | --- |
| "Our method is state of the art" | Asks which tuned baselines, which regime | "Improves over tuned X on Y under stated budget" |
| "Provably converges" | Checks whether assumptions match experiments | "Converges at rate R under assumptions A, observed in-regime" |
| "Generalizes broadly" | Wants scaling evidence | "Holds across the model sizes and datasets we test" |

## Worked vignette: framing an optimizer contribution

For a paper pairing a non-convex convergence theorem with deep-learning benchmarks, the introduction
states the optimization problem, names the rate prior methods cannot match, gives the theorem and the
benchmark win as separable contributions, and bounds the claim to the assumption regime. This lets a
loaded reviewer extract soundness, originality, and significance from one column without hunting the
appendix.

## Output format

```text
[Claim before] <original>
[Claim after] <ICML-calibrated version>
[Evidence anchor] <proof/experiment/baseline/artifact>
[Impact statement note] <needed addition or narrowing>
[8-page compression] <what to cut or move>
```