International Conference On Automated Machine Learning

---
name: international-conference-on-automated-machine-learning
description: "Use when targeting International Conference on Automated Machine Learning (AutoML Conference) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for automated ML."
category: data-science-and-ml
source_repo: brycewang-stanford/Awesome-Journal-Skills
source_path: "Computer-Science-Conference-Skills/skills/international-conference-on-automated-machine-learning/SKILL.md"
source_url: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Computer-Science-Conference-Skills/skills/international-conference-on-automated-machine-learning/SKILL.md
---


# International Conference on Automated Machine Learning (AutoML Conference)

## Conference positioning

International Conference on Automated Machine Learning (AutoML Conference) is a top computer-science conference venue for neural architecture search, hyperparameter optimization, AutoML systems, meta-learning, and benchmarking. It rewards an automation paper that improves the search process, not just the final leaderboard number. Treat this skill as a **fit / venue-selection / re-framing** tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.

Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in `../../resources/conference-roster.md` and `../../resources/official-source-map.md`.

## When to trigger

- The author names AutoML Conference / International Conference on Automated Machine Learning as the target venue.
- A manuscript in neural architecture search needs a conference-fit read before being formatted or submitted.
- The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative.
- The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue.

## Scope & topic fit

- Core fit: neural architecture search, hyperparameter optimization, AutoML systems, meta-learning, and benchmarking.
- Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis.
- The paper should explain why the result matters to AutoML Conference's reviewers, not just why it is interesting to the authors' lab or product context.
- Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness.
- If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence.

## Venue-specific calibration

- Reviewer lens: Treat AutoML Conference as a automated ML venue whose reviewers expect the scope and evidence to match its own community. Do not submit a generic CS paper until the introduction names the exact subcommunity, contribution type, and proof or empirical standard.
- Contribution hook to foreground: the venue-specific contribution bar.
- Scope vocabulary to use naturally in the abstract and introduction: neural architecture search, hyperparameter optimization, AutoML systems, meta-learning, and benchmarking.
- Distinctive fingerprint for reviewer calibration: neural, architecture, search, hyperparameter, optimization, automl, meta-learning, benchmarking, venue-specific, contribution, automated.
- Official anchor domain: automl.cc. Quote annual rules only after opening that source and the current-year CFP/author kit.

## Close-neighbor routing guardrail

- Use this profile only when the manuscript's central contribution is genuinely in automated ML
  and the author can say why AutoML Conference reviewers are the primary audience, not merely
  a convenient deadline.
- Closest roster neighbors to compare before final routing: `conference-on-machine-learning-and-
  systems` (MLSys), `conference-on-lifelong-learning-agents` (CoLLAs), `conference-on-health-
  inference-and-learning` (CHIL), `machine-learning-for-health` (ML4H). Break ties by
  contribution type, evidence shape, reviewer community, and the current official CFP from
  automl.cc.

## What distinguishes this venue from its closest siblings

- **What the AutoML Conference is.** The venue for **automated machine learning** — neural architecture search, hyperparameter optimization, and meta-learning pipelines.
- **vs CoLLAs.** CoLLAs targets **lifelong/continual** learning agents; bring AutoML/NAS/HPO contributions here.
- **vs NeurIPS/ICML/ICLR.** The general ML flagships cover AutoML topics too; pick this venue when the AutoML community and benchmarks are central.

## AutoML-specific routing detail

- Prefer AutoML when the novelty improves the search, selection, tuning, meta-learning, NAS, benchmark, or system pipeline that automates ML decisions.
- Route lifelong-agent adaptation to CoLLAs, production training/inference infrastructure to MLSys, and general representation-learning algorithms to the ML flagships unless automation is the central object.
- The evidence should isolate search efficiency, robustness, transfer across tasks, and compute budget tradeoffs instead of reporting only the final model score.

## Method & evidence bar

- Compare against current strong baselines and explain exactly what changes in the algorithm, objective, data, or inference procedure.
- Report ablations that isolate the claimed mechanism; do not rely on aggregate benchmark wins alone.
- Document data, compute, hyperparameters, model selection, and failure cases so the result can be reviewed as science rather than demo output.
- For AutoML Conference, the evidence must support the venue-specific signature: an automation paper that improves the search process, not just the final leaderboard number.
- Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.

## Structure & house style

- Frame the contribution as a reusable idea: method, theory, benchmark, dataset, system, or socio-technical finding.
- Separate main claims from exploratory results; reviewers at top AI venues punish overclaiming and hidden cherry-picking.
- Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles.
- The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim.
- Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution.

## Official-cycle checklist

- Open the live official venue page: https://automl.cc/
- Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements.
- Confirm the review workflow and portal: OpenReview / CMT / HotCRP / PCS / START or society portal, as specified for the current cycle.
- Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms.
- If the live official instructions conflict with this skill, the official instructions win.

## Pre-submission self-check

- [ ] One sentence states why this manuscript belongs at AutoML Conference, using the venue's scope rather than generic "top conference" language.
- [ ] The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support.
- [ ] Related work includes the nearest current-cycle automated ML papers and explains the technical delta.
- [ ] The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements.
- [ ] The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links.

## Common desk-reject triggers

- Leaderboard-only novelty with weak explanation of why the method works.
- Unclear data contamination, missing baselines, or evaluation that cannot be reproduced.
- Claims about safety, fairness, health, or society without matching evidence and limitations.
- Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
- A contribution framed for a neighboring field while giving AutoML Conference reviewers too little technical or empirical substance.

## Re-routing decision

If the paper misses AutoML Conference's bar, compare against `neural-information-processing-systems` / `international-conference-on-machine-learning` / `international-conference-on-learning-representations` / `aaai-conference-on-artificial-intelligence`. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.

## Output format

```text
[Fit] High / Medium / Low (one-line reason)
[Target] International Conference on Automated Machine Learning (AutoML Conference)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>
```

---

**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Computer-Science-Conference-Skills/skills/international-conference-on-automated-machine-learning/SKILL.md`