Skill UI

A comprehensive guide for authors preparing manuscripts for the International Conference on Artificial Intelligence and Statistics (AISTATS). It advises on whether the paper's contribution is statistically grounded and theory-backed, guiding framing, evidence presentation, and understanding reviewer expectations in statistical machine learning.

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.

This comprehensive guide outlines best practices for packaging and releasing scientific artifacts—including code, training configurations, demonstration data, and policy checkpoints—for robotics research papers. It details how to prepare materials for anonymous reviewer assessment and for durable public release, ensuring the work is fully reproducible by the academic community.

A comprehensive guide for authors preparing a single-page academic rebuttal for top-tier AI conferences (e.g., CoRL). It details how to structure the response, focusing on maximizing impact through new, compact evidence (tables/figures) rather than verbose prose. Learn to triage reviewer comments, maintain a professional tone for public record, and optimize space for maximum score movement.

A comprehensive pre-submission audit guide for authors submitting papers to EACL (Computational Linguistics). It covers critical areas such as adherence to format gates (8/4 content pages, required Limitations section), strict anonymity requirements, mandatory declarations (Responsible NLP, preprint status), and crucial advice regarding the single, non-fallback submission cycle. Use this before uploading to the ACL Rolling Review cycle.

A comprehensive project management skill designed for authors preparing papers for the EACL conference. It maps out the entire single-cycle submission process, covering backward planning from the ARR deadline, rebuttal strategies, commitment decisions, and final camera-ready stages. Essential for navigating highly structured academic publication timelines and minimizing risk.

A comprehensive guide for authors preparing the single, short, double-blind rebuttal (Author Response) required during major AI conference review cycles (e.g., IJCAI-ECAI). It teaches authors how to precisely correct factual misreadings, supply missing evidence, and structure their arguments effectively within strict length limits, focusing only on verifiable facts rather than new claims or philosophical arguments.

This guide provides comprehensive instructions for preparing and submitting supplementary material for computer vision conferences like ECCV. It details how the supplement should support the main paper's claims without adding new results, covering topics such as qualitative result visualization, detailed ablation studies, code archive scrubbing, and ensuring full anonymity across all uploaded assets.

This comprehensive guide assists authors in strategically positioning their computer vision manuscripts for the European Conference on Computer Vision (ECCV). It covers conference fit assessment, manuscript re-framing, required evidence (e.g., ablations, baselines, failure modes), and structural advice to ensure the work aligns with ECCV's scope and current review policies. Use this tool for venue selection and submission preparation.

A comprehensive guide to preparing accountability artifacts for AI research papers, particularly for venues like FAccT. It covers the necessary documentation genres—including datasheets for datasets, model cards for models, and impact assessments—to ensure transparency, verifiability, and accountability regarding claims of fairness and harm mitigation in AI systems. The focus is on making research outputs rigorously traceable and ethically responsible.

This comprehensive guide outlines the critical steps required for authors preparing an accepted paper for ACM FAccT proceedings. It details the transition from anonymous drafts to the final, published 'camera-ready' format, covering mandatory de-anonymization (restoring author details and adding Positionality statements), switching to the specific ACM metadata standards (ORCID, CCS concepts), completing required documentation (datasheets/model cards), and adhering to complex publication timelines and rights requirements.

This guide details the best practices for packaging, documenting, and releasing research artifacts (code, models, data) for top-tier ML conferences. It covers two stages: preparing a non-identifiable archive for anonymous review, and managing the public release runway, focusing on reproducibility, licensing, and sustained maintenance.