Skill UI

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 guide instructs authors on how to package theoretical proofs for submission to COLT (Conference on Learning Theory). Since COLT does not have a formal artifact track, it emphasizes treating the proof appendix as the primary 'artifact.' It details best practices for creating a verifiable proof ledger, rigorously tracking constants and external results, and managing supplementary code to ensure maximum academic credibility.

A comprehensive guide for drafting author responses to highly technical peer reviews in top-tier theoretical conferences. Learn how to repair perceived gaps, address objections regarding proof correctness, tightness, and novelty-of-technique, while adhering to specific academic policies and structure.

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 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.

A comprehensive project management framework for authors submitting theoretical papers to major conferences (e.g., COLT). This guide outlines the entire lifecycle, emphasizing backward planning from the deadline, milestone setting (Result Freeze, Verification Pass), and critical quality checks, including proof assembly, anonymity sweeps, and rebuttal response. It is designed to mitigate common academic writing risks and ensure a robust submission process.

This comprehensive guide provides detailed instructions for revising academic papers, specifically in the field of learning theory, for the COLT conference. It teaches authors how to structure the argument rigorously, ensuring the model is unambiguous, mastering the informal-to-formal theorem pairing, and maintaining mathematical precision (quantifier and constant hygiene).

This skill reconstructs the full narrative behind a piece of code by analyzing local Git history. Instead of just naming the last author (like git blame), it traces the file's origin, tracks subsequent changes, identifies related companion files, and extracts intent clues from commit messages. Use this when you need to know *why* code exists before refactoring, making risky edits, or understanding complex system behavior.

A sophisticated guide for academic writers on structuring the literature review section of communication science manuscripts. It teaches authors how to move beyond simple summaries by actively positioning their hypotheses against existing empirical debates, identifying precise knowledge gaps, and adjudicating rival theoretical accounts. This skill ensures the work reads as a cumulative, theory-testing contribution suitable for top-tier academic journals.

A comprehensive guide to passing the rigorous, multi-stage peer-review process of top-tier academic journals. This skill helps authors understand the double-anonymized screening criteria, from initial editorial fit checks (quantitative/theory-testing) to satisfying multiple independent expert reviewers. Use this to stress-test your manuscript's methodology, measurement validity, and theoretical contribution before submission.