Skill UI

Formats tables, figures, formulas and pseudocode for a Chinese Journal of Management Science manuscript: three-line tables with bolded optima, uniformly numbered equation-editor formulas, algorithm boxes, convergence and sensitivity plots, all inside the 12-page limit, plus a pre-submission checklist.

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 comprehensive guide for ML research groups planning multi-venue conference submissions (e.g., COLM). It details the critical path of academic publishing, emphasizing backward induction from hard deadlines (abstract, paper, rebuttal, decision). The workflow covers compute budget allocation, co-author coordination, claim tracking, and managing the technical pipeline from initial topic selection to final presentation, ensuring reproducibility and timely execution in fast-paced AI research cycles.

This comprehensive guide assists researchers in converting an accepted, anonymous draft for the Conference on Learning Theory (COLT) into the final, polished camera-ready version for publication in PMLR proceedings. It covers critical steps such as de-anonymization (restoring author details, acknowledgements), consolidating all promised revisions into the main text, verifying PMLR metadata and BibTeX consistency, and preparing the full archival PDF.

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 comprehensive audit checklist guides authors preparing a paper for the COLT Conference on Learning Theory. It details crucial requirements such as the 12-page PMLR format, single-PDF assembly, and double-anonymous formatting. It helps authors preemptively detect common submission flaws, including parallel-submission violations and formatting errors, ensuring the manuscript meets high academic standards.

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.

A comprehensive guide for positioning continual learning and lifelong agent research for the Conference on Lifelong Learning Agents (CoLLAs). This tool helps authors strategically re-frame their work, assess its fit, and optimize the narrative to meet the specific evidence bar of top CS venues focusing on nonstationary learning, adaptation, and memory. It covers scope calibration, required evidence types, and comparison with related ML conferences.