icml-related-work

ICML Related Work

Use this when novelty, incremental contribution, or concurrent-submission handling is the risk. ICML 2026 treats related concurrent ICML submissions with overlapping authors as prior work.

Required coverage

• Closest ML methods, theory, datasets, benchmarks, and evaluation papers.
• Neighboring venue papers from NeurIPS, ICLR, AISTATS, UAI, COLT, MLSys, KDD, ACL, CVPR, and other relevant areas.
• Related concurrent ICML submissions by overlapping authors; cite anonymously and include PDFs in supplementary material when a reasonable reviewer would expect them.
• Workshop papers without published proceedings generally do not trigger dual-submission violation, but their relationship still needs honest positioning.
• Very recent public work close to the full-paper deadline can be treated as concurrent, but good judgment and subfield norms matter.

Delta paragraph

<Prior work> addresses <problem> using <mechanism>. It leaves <specific gap>.
Our submission differs by <technical delta>, and this matters because <evidence>.

Novelty-pushback table (ICML reviewer reflexes)

ICML reviewers triage novelty fast because the load is heavy and the first PMLR page must already signal the delta. Map the likely objection to a venue-specific repair.

Reviewer objection	What it means at ICML	Repair before submission
"This is a known trick rebranded"	Mechanism overlaps a COLT/NeurIPS result	Cite it, state the assumption or rate that differs, move the delta to page 1
"Concurrent work already does this"	Overlapping-author ICML paper or recent arXiv	Cite anonymously, attach the PDF, give a one-line technical separation
"Optimization angle is incremental"	A new step-size or proximal variant	Show the regime where prior rates fail and yours holds, plus a tuned-baseline plot
"Benchmark-only contribution"	No mechanism, just leaderboard	Add an ablation tying the gain to the proposed component

Worked vignette: a new optimizer with convergence theory

Suppose the paper proposes an adaptive-step method with a non-convex convergence guarantee plus deep-learning benchmarks. The related-work risk is that reviewers know Adam, AdaGrad, Lion, and the escaping-saddle and variance-reduction literature. Position it by separating the assumption set (smoothness, bounded variance, PL condition) your rate needs from what neighbors assume, naming the closest COLT/NeurIPS optimization theory, and conceding which benchmarks are shared rather than claiming a blanket win. The delta paragraph then reads as a precise rate-and-regime statement, not a list of beaten methods.

Output format

[Closest work] <3-5 papers or clusters>
[Concurrent ICML handling] none / cite anonymously / include PDF / split papers
[Incrementality risk] high / medium / low
[Technical delta] <one sentence>
[Related-work rewrite] <paragraph>