Skill UI

This guide provides a comprehensive audit checklist for submissions to major AI/ML conferences like ICLR. It covers critical areas including maintaining double-blind anonymity, adherence to page limits, required LLM disclosure, and mitigating desk-reject risks. Use this before submission deadlines to ensure your paper is fully compliant and ready for OpenReview.

This guide assists researchers in determining the optimal venue for their theoretical machine learning contributions. It provides a framework for evaluating whether a result is best suited for COLT, or if it belongs in other top-tier conferences (e.g., NeurIPS, ICML) or journals (e.g., JMLR). It emphasizes differentiating between rigorous mathematical proofs (bounds, separations) and purely empirical findings.

This guide provides essential instructions for authors submitting research to the ECAI conference, focusing on creating credible and reproducible artifacts. It strongly cautions authors about misconceptions regarding separate artifact badges, emphasizing that credibility must be built into the paper and supplement for direct reviewer judgment. Guidelines are provided for matching the artifact (code, proofs, data) to the specific contribution type (Theory, ML, Optimization).

This guide helps researchers determine if their Machine Learning project is suitable for ICLR, or if a different top-tier conference (such as NeurIPS, ICML, or CVPR) is a better fit. It emphasizes the need for deep representation-learning insights, mechanistic understanding, and theoretical contributions over mere application performance.

This guide helps transform technically sound machine learning drafts into highly readable, conference-ready papers suitable for ICLR and OpenReview. It emphasizes structuring the core learning-representation insight upfront, clarifying contribution types (method, theory, benchmark), optimizing for public skimming, and providing clear reviewer navigation paths. Use this when ensuring maximum impact and clarity in a challenging academic review environment.

A comprehensive guide for authors on drafting strategic, anonymous rebuttals and discussion replies for top-tier Machine Learning conferences like ICML. Learn how to triage reviewer objections by focusing on soundness, originality, significance, clarity, ethics, and reproducibility, adhering strictly to double-blind review constraints and maximizing impact in limited discussion rounds.

This guide provides a comprehensive framework for stress-testing Machine Learning experimental evidence before submission or rebuttal. It covers critical areas such as establishing strong baselines, conducting mechanism-isolating ablations, reporting variance, checking for data leakage, and disclosing compute costs. Use this to ensure your ML claims are scientifically sound, robust, and fair against rigorous academic scrutiny.

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.

A comprehensive guide for researchers detailing how to strengthen the reproducibility of AI/ML papers for top-tier conferences like ICML. It provides an evidence checklist covering data sources, anonymized code, random seeds, compute budget disclosure, and theoretical assumptions, ensuring claims are robust and verifiable.

A comprehensive guide for researchers deciding the best venue for their machine learning manuscript. It helps evaluate whether a paper is a strong fit for ICML, or if it should be rerouted to specialized tracks, other top-tier conferences (NeurIPS, ICLR), or academic journals based on its core contribution type (theory, method, application) and novelty.

This skill serves as a comprehensive router for managing the entire lifecycle of an ICML manuscript. It guides users through every phase, including topic selection, drafting, artifact preparation, OpenReview submission, double-blind review, author response, and final camera-ready publication. It helps track risks, evidence, and official conference policies.

This comprehensive guide teaches researchers how to structure and refine machine learning papers for top-tier conferences like ICML. It focuses on achieving clarity, soundness, originality, and significance within strict page limits, providing actionable strategies to ensure the research withstands rigorous reviewer scrutiny.