Skill UI

A comprehensive guide for structuring research artifacts (code, models, datasets, media) for ACM Multimedia conferences. It details the distinct requirements for anonymous review packages versus permanent public releases, offering guidelines on ethics, licensing, reproducibility, and track-specific documentation.

A comprehensive guide for drafting professional and strategic rebuttals for major academic conferences, such as ACM Multimedia. Learn how to triage reviewer comments, structure arguments for the Area Chair, address factual errors, and incorporate small, confirmatory results while maintaining anonymity and adhering to strict policy guardrails.

A guide for researchers submitting to conferences like ACM Multimedia, detailing how to achieve robust result reproducibility. It covers the necessary steps—including logging code commits, data versions, media preprocessing parameters, and hardware details—to allow independent reviewers to successfully rebuild the reported findings and achieve official reproducibility badges.

A comprehensive checklist for auditing academic submissions to ACM Multimedia (ACM MM). Covers thematic area selection, formatting constraints (6-8 page limit), enforcing double-blind anonymity, managing OpenReview mechanics, and identifying common desk-reject triggers. Essential for authors preparing technical papers for top-tier conferences.

A comprehensive guide for authors preparing the supplementary material for multimedia conferences (e.g., ACM MM). It details how to structure the content, separating core arguments (Body) from supporting evidence (Supplement). Learn best practices for packaging videos, audio, and interactive demos, ensuring technical compatibility (H.264/MP4), maintaining anonymity for double-blind review, and creating a legible, reviewer-friendly submission index.

This comprehensive guide outlines the entire end-to-end workflow for submitting and presenting at ACM Multimedia (ACM MM). It breaks down the complex process into distinct phases—from initial thematic scoping and track selection, through the OpenReview abstract submission, paper and supplement delivery, rebuttal, decision, camera-ready preparation, and final conference presentation. It provides actionable advice, risk mitigation strategies, and re-targeting options for both success and rejection.

This comprehensive guide details the structured method for drafting author responses to peer reviewers' comments for major conferences like AISTATS. It covers best practices for addressing statistical validity, novelty, and technical concerns in a constrained, anonymous, text-only format. Learn how to triage objections, anchor claims to existing evidence, and handle specific statistical pushback patterns.

This comprehensive guide provides authors with a detailed audit checklist for preparing submissions for major AI conferences like AISTATS. It covers critical pre-submission requirements, including maintaining double-blind anonymity, adhering to strict page limits, managing OpenReview details, and ensuring reproducibility. Use this checklist before the abstract or full-paper deadline to mitigate desk-reject risks.

This guide provides comprehensive best practices for preparing supplementary materials for major academic conferences like AISTATS. It details the proper structure and handling of appendices, mathematical proofs, code archives, and additional data tables, ensuring that the main paper remains fully understandable even if the supplementary material is ignored by reviewers. Essential for publishing high-quality, robust research.

A comprehensive project management guide for the entire academic lifecycle of submitting a paper to a top-tier conference like AISTATS. It maps all crucial milestones—from initial venue fit and evidence freezing to abstract submission, review discussion, and final camera-ready publication—providing backward-planning heuristics and risk assessment to ensure timely and high-quality submissions.

This guide provides comprehensive rules for authors revising technical papers for top AI/Statistics conferences like AISTATS. It emphasizes achieving statistical rigor, explicitly stating assumptions, pairing every claim with proof or evidence, ensuring conciseness (e.g., adhering to an 8-page limit), and maintaining double-blind academic style. Perfect for PhD students or researchers preparing manuscripts for highly technical review.

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.