Skill UI

A comprehensive guide for authors targeting the British Machine Vision Conference (BMVC). This skill helps assess whether a computer vision manuscript fits the venue, providing expert advice on framing, necessary evidence gaps, structural improvements, and differentiating the work from related CV conferences (e.g., CVPR, ICCV). It ensures the contribution is clear and highly specialized for specialist reviewers.

A comprehensive guide for strengthening causal identification strategies in empirical finance and economics. It covers advanced quasi-experimental designs like Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), Propensity Score Matching (PSM), and Double Machine Learning (DML). Use this framework to stress-test your research design, address common econometric pitfalls, and ensure your paper meets the rigorous standards of top academic journals.

This comprehensive guide provides expert protocols for writing the 'Related Work' section in Machine Learning papers. It teaches authors how to maintain 'versioned honesty' by meticulously triaging the literature into distinct lanes (e.g., method lineage, model artifacts, evaluation infrastructure). It covers advanced topics like managing concurrent work, citing arXiv status accurately, and avoiding self-citation violations, ensuring the paper is positioned rigorously within the rapidly evolving academic field.

A comprehensive guide detailing how to structure and populate the supplementary material section of technical research papers, especially in NLP and Machine Learning. It distinguishes between load-bearing elements (main text) and audit trails (appendix), covering prompt verbatim, decoding configurations, full result grids, and code anonymization protocols to ensure high reproducibility and academic rigor.

A detailed guide for authors submitting theoretical machine learning papers (like COLT) regarding the appropriate inclusion and design of numerical simulations. It emphasizes that numerics should only visualize proved bounds, phase transitions, or separations, maintaining the focus on theoretical proofs rather than empirical data. Provides rules for plotting, reporting variability, and maintaining academic honesty.

A detailed guide for authors structuring the 'Related Work' section of theoretical machine learning papers. It shifts focus from qualitative prose to quantitative comparison, instructing users to build explicit tables comparing prior bounds versus new bounds, and systematically covering diverse literature lanes like complexity theory, statistics, and open problems to rigorously establish novelty.

This guide outlines the stringent standards for academic paper reproducibility in theoretical machine learning (e.g., COLT). It provides a comprehensive pre-submission audit checklist covering formal proof completeness, assumption bookkeeping, constant tracking, and ensuring numerical illustrations are fully traceable. Use this methodology to strengthen your theoretical claims and withstand rigorous peer review.

This guide details the rigorous, mathematically focused peer-review pipeline for top ML conferences like COLT (Conference on Learning Theory). It explains the double-anonymous review system, how reviewers assess correctness, significance, and novelty, and provides actionable advice on structuring rebuttals and interpreting final decisions for researchers aiming for top-tier academic publication.

This skill guides authors in assessing the suitability and optimal framing of their machine learning research in the health domain for top-tier academic conferences like CHIL. It ensures the manuscript meets the specific evidence standards, emphasizing clinical grounding, fairness, robustness, and deployment constraints, transforming general CS narratives into specialized health ML contributions.

This guide helps authors position their machine learning systems manuscripts for the rigorous Conference on Machine Learning and Systems (MLSys). It details the venue's specific focus on system-level contributions, including training/inference infrastructure, compilers, data systems, and hardware-aware ML. Learn how to frame your work to appeal to systems reviewers by emphasizing measurable impacts on capability, cost, reliability, or deployment scale.

This comprehensive style guide is written for authors submitting research papers to the CoRL conference. It provides specific directives on structuring the narrative to satisfy two distinct audiences: machine learning theorists and robotics engineers. It emphasizes quantifying all claims with concrete evidence scales (e.g., success rates, frequency), structuring the argument within a limited page count, and properly integrating mandatory sections like Limitations.

This guide provides comprehensive instructions for creating a robust, reproducible artifact package for database systems research. It details best practices including containerization, pinning workloads, generating a permanent DOI-archived record, and designing the package to be successfully built and demonstrated on a reviewer's clean machine within a tight time budget. The goal is to strengthen scientific rigor and ensure permanent, open-access citation.