Conference On Machine Learning And Systems

---
name: conference-on-machine-learning-and-systems
description: "Use when targeting Conference on Machine Learning and Systems (MLSys) 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 ML systems."
category: data-science-and-ml
source_repo: brycewang-stanford/Awesome-Journal-Skills
source_path: "Computer-Science-Conference-Skills/skills/conference-on-machine-learning-and-systems/SKILL.md"
source_url: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Computer-Science-Conference-Skills/skills/conference-on-machine-learning-and-systems/SKILL.md
---


# Conference on Machine Learning and Systems (MLSys)

## Conference positioning

Conference on Machine Learning and Systems (MLSys) is a top computer-science conference venue for machine learning systems, training/inference infrastructure, compilers, data systems, and hardware-aware ML. It rewards a paper where the systems contribution changes ML capability, cost, reliability, or deployment scale. 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 MLSys / Conference on Machine Learning and Systems as the target venue.
- A manuscript in machine learning systems 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: machine learning systems, training/inference infrastructure, compilers, data systems, and hardware-aware ML.
- 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 MLSys'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: Read reviewers as systems builders for ML. Training, inference, compilers, accelerators, data pipelines, and deployment constraints must be measured, not only described.
- Contribution hook to foreground: the venue-specific contribution bar.
- Scope vocabulary to use naturally in the abstract and introduction: machine learning systems, training/inference infrastructure, compilers, data systems, and hardware-aware ML.
- Distinctive fingerprint for reviewer calibration: machine, learning, training, inference, infrastructure, compilers, data, hardware-aware, venue-specific, contribution, mlsys.
- Official anchor domain: mlsys.org. 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 ML systems
  and the author can say why MLSys reviewers are the primary audience, not merely a convenient
  deadline.
- Closest roster neighbors to compare before final routing: `uncertainty-in-artificial-
  intelligence` (UAI), `conference-on-learning-theory` (COLT), `conference-on-lifelong-
  learning-agents` (CoLLAs), `international-conference-on-automated-machine-learning` (AutoML
  Conference). Break ties by contribution type, evidence shape, reviewer community, and the
  current official CFP from mlsys.org.

## MLSys-specific routing detail

- Prefer MLSys when the contribution changes ML training, inference, serving, compiler/runtime behavior, data pipeline, hardware utilization, reliability, cost, or deployment scale.
- Route autonomous-agent interaction, negotiation, incentives, or decentralized decision-making to AAMAS; route algorithmic ML without systems constraints to NeurIPS/ICML/ICLR.
- MLSys evidence should include system bottlenecks, throughput/latency/cost/reliability tradeoffs, and realistic ML workloads rather than only benchmark accuracy.

## Method & evidence bar

- Build the artifact or prototype far enough that the core design can be measured under realistic workloads.
- Use appropriate baselines, sensitivity analyses, and workload characterization; systems reviewers look for hidden bottlenecks.
- Separate engineering effort from research contribution: name the abstraction, mechanism, or tradeoff.
- For MLSys, the evidence must support the venue-specific signature: a paper where the systems contribution changes ML capability, cost, reliability, or deployment scale.
- Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.

## Structure & house style

- Start from a systems pain point and show why existing abstractions fail.
- Use evaluation sections that answer research questions, not a tour of every benchmark run.
- 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://mlsys.org/
- 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: the current USENIX/ACM/IEEE author kit, artifact policy, and submission system.
- 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 MLSys, 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 ML systems 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

- Toy implementation or microbenchmark-only evidence for a systems claim.
- No comparison to mature systems or no explanation of deployment constraints.
- Performance gains with unclear workload representativeness.
- Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
- A contribution framed for a neighboring field while giving MLSys reviewers too little technical or empirical substance.

## Re-routing decision

If the paper misses MLSys's bar, compare against `acm-symposium-on-operating-systems-principles` / `usenix-symposium-on-operating-systems-design-and-implementation` / `usenix-symposium-on-networked-systems-design-and-implementation` / `acm-sigcomm`. 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] Conference on Machine Learning and Systems (MLSys)
[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/conference-on-machine-learning-and-systems/SKILL.md`