Skill UI

A comprehensive guide for researchers managing the complex, year-long cycle of major computer vision conferences (like ICCV). Covers critical stages from the initial 'autumn fork' decision and submission deadlines to rebuttal, final decisions, and post-conference strategies, including international travel logistics and fallback plans.

This comprehensive guide outlines the critical steps for authors preparing their accepted ICLR paper for camera-ready submission. It covers de-anonymization procedures, incorporating all fixes promised during the discussion period, completing OpenReview metadata, and assembling all necessary presentation assets, including posters, slide decks, and videos, according to current conference guidelines.

This guide provides comprehensive best practices for designing and auditing empirical experiments for top-tier ML conferences. It covers necessary ablations, robustness checks (e.g., data shift, seeds), fair baselines, and detailed compute reporting to ensure that reported gains are scientifically genuine, reproducible, and address common reviewer skepticism.

A comprehensive guide for academic authors detailing how to establish and maintain rigorous reproducibility in Machine Learning research papers (e.g., ICLR). It instructs on mapping every central claim to verifiable evidence, including recording seeds, variance, specific compute budgets (training vs. inference), data preprocessing scripts, and ethical considerations. The statement must function as a public contract for the scientific community.

Provides a comprehensive guide for planning strategy within major academic conference review systems, such as OpenReview. It details the double-blind review lifecycle, author response strategies, managing public vs. private discussions, and understanding how permanent visibility impacts decision-making. Ideal for authors and reviewers preparing for high-stakes academic submissions.

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 skill manages the end-to-end project lifecycle for academic conference submissions, specifically tailored for major venues like ICLR. It provides a structured timeline, defining critical milestones from initial topic selection and abstract submission through paper deadlines, public discussion, and final camera-ready versions. Use it to sequence research milestones, manage evidence paths, and ensure compliance with conference rules like anonymity audits and strict deadlines.

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.