Ml Paper Writing

# Conference Paper Checklists

This reference documents the mandatory checklist requirements for major ML/AI conferences. All major venues now require paper checklists—missing them results in desk rejection.

**For systems conference checklists (OSDI, NSDI, ASPLOS, SOSP)**, see the [systems-paper-writing](../../systems-paper-writing/) skill.

## Contents

- [NeurIPS Paper Checklist](#neurips-paper-checklist)
- [ICML Paper Checklist](#icml-paper-checklist)
- [ICLR Requirements](#iclr-requirements)
- [ACL Requirements](#acl-requirements)
- [Universal Pre-Submission Checklist](#universal-pre-submission-checklist)

## NeurIPS Paper Checklist

### Mandatory Components

All NeurIPS submissions must include a completed paper checklist. Papers lacking this element face **automatic desk rejection**. The checklist appears after references and supplemental material, outside the page limit.

### 16 Required Checklist Items

#### 1. Claims Alignment
Authors must verify that abstract and introduction claims match theoretical and experimental results, with clearly stated contributions, assumptions, and limitations.

**What to check:**
- [ ] Abstract claims match actual results
- [ ] Introduction doesn't overclaim
- [ ] Contributions are specific and falsifiable

#### 2. Limitations Discussion
Papers should include a dedicated "Limitations" section addressing strong assumptions, robustness to violations, scope constraints, and performance-influencing factors.

**What to include:**
- [ ] Dedicated Limitations section
- [ ] Honest assessment of scope
- [ ] Conditions where method may fail

#### 3. Theory & Proofs
Theoretical contributions require full assumption statements and complete proofs (main paper or appendix with proof sketches for intuition).

**What to check:**
- [ ] All assumptions stated formally
- [ ] Complete proofs provided (main text or appendix)
- [ ] Proof sketches for intuition in main text

#### 4. Reproducibility
Authors must describe steps ensuring results verification through code release, detailed instructions, model access, or checkpoints appropriate to their contribution type.

**What to provide:**
- [ ] Clear reproducibility statement
- [ ] Code availability information
- [ ] Model checkpoints if applicable

#### 5. Data & Code Access
Instructions for reproducing main experimental results should be provided (supplemental material or URLs), including exact commands and environment specifications.

**What to include:**
- [ ] Exact commands to run experiments
- [ ] Environment specifications (requirements.txt, conda env)
- [ ] Data access instructions

#### 6. Experimental Details
Papers must specify training details: data splits, hyperparameters, and selection methods in the main paper or supplementary materials.

**What to document:**
- [ ] Train/val/test split details
- [ ] All hyperparameters used
- [ ] Hyperparameter selection method

#### 7. Statistical Significance
Results require error bars, confidence intervals, or statistical tests with clearly stated calculation methods and underlying assumptions.

**What to include:**
- [ ] Error bars or confidence intervals
- [ ] Number of runs/seeds
- [ ] Calculation method (std dev vs std error)

#### 8. Compute Resources
Specifications needed: compute worker types (CPU/GPU), memory, storage, execution time per run, and total project compute requirements.

**What to document:**
- [ ] GPU type and count
- [ ] Training time per run
- [ ] Total compute used

#### 9. Ethics Code Compliance
Authors confirm adherence to the NeurIPS Code of Ethics, noting any necessary deviations.

**What to verify:**
- [ ] Read NeurIPS Code of Ethics
- [ ] Confirm compliance
- [ ] Note any deviations with justification

#### 10. Broader Impacts
Discussion of potential negative societal applications, fairness concerns, privacy risks, and possible mitigation strategies when applicable.

**What to address:**
- [ ] Potential negative applications
- [ ] Fairness considerations
- [ ] Privacy implicati