Positioning Novelty in ML Research Papers

v20260724

icml-related-work

This guide provides structured methodologies for authors to position their research within the context of existing literature and concurrent submissions at top-tier machine learning conferences (e.g., ICML, NeurIPS). It emphasizes identifying the technical delta, addressing novelty concerns, and properly citing related work to maximize acceptance chances.

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Overview

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>

Info

Category Artificial Intelligence

Name icml-related-work

Version v20260724

Size 3.13KB

Source brycewang-stanford/Awesome-Journal-Skills

Updated At 2026-07-28