icml-topic-selection

ICML Topic Selection

Use this before committing to ICML. ICML rewards original, rigorous machine-learning research of

significant interest to the ML community. It is not the best route for every AI application or

position argument.

Strong fit

• A core ML method, theory, optimization, probabilistic model, RL algorithm, evaluation method,

systems contribution, or trustworthy-ML result.

• A use-inspired paper where the ML technique, evaluation, or insight is itself important to the ML

community.

• A theory paper with clear assumptions and meaningful implications.
• An empirical study that improves how ML is evaluated, reproduced, scaled, or understood.
• A paper that can show soundness, originality, significance, clarity, and reproducibility within

ICML's format.

Weak fit

• A domain deployment with little ML novelty.
• A benchmark win without mechanism or fair baselines.
• A position or argument paper better suited to the ICML Position Papers track.
• A replication, survey, dataset report, or engineering system better matched to another venue.
• A paper that needs more than appendices or supplement to make the main contribution intelligible.

Routing decisions

• Main-track ICML: rigorous ML contribution with strong evidence.
• Position Papers: thesis-driven argument about the field rather than a standard research result.
• NeurIPS/ICLR/AISTATS/UAI/COLT/MLSys: choose based on theory, representation learning, statistics,

uncertainty, learning theory, or systems emphasis.

• TMLR/JMLR: choose for journal-style depth, long revision cycles, or results needing more space.

Fit-versus-reroute table

| Manuscript shape | ICML verdict | Better route if not ICML |

| --- | --- | --- |

| New method with theory plus tuned benchmarks | Strong main-track fit | - |

| Pure learning-theory result, no experiments | Fits if significant | COLT for theory depth |

| Field-level argument or call for rigor | Reroute | ICML Position Papers track |

| Application with little ML novelty | Weak | Domain venue or applied track |

| Long result needing more than 8 pages | Reconsider | TMLR or JMLR |

Worked vignette: where does the optimizer paper go

A new adaptive-step method has a non-convex convergence theorem and deep-learning benchmarks. This is

a textbook ICML main-track fit because the ML mechanism, the rate, and the empirical gain are all of

broad interest. If the same authors instead wrote an essay arguing the community over-relies on

adaptive methods, that belongs in the Position Papers track, which uses a separate call and

OpenReview site; check the current year's CFP for both tracks before deciding.

Output format

[Fit] High / Medium / Low
[Recommended route] ICML main / ICML position / workshop / another conference / journal
[Contribution type] method / theory / evaluation / systems / trustworthy ML / application-driven / position
[Why ICML] <one sentence>
[Upgrade needed] <evidence, framing, related work, artifacts, impact, or reroute>