Skill UI

A guide for academic writers on how to succinctly position a research contribution against the single closest prior work in highly constrained formats (e.g., AER: Insights). It teaches authors to state the novelty (the 'delta') in one or two paragraphs, avoiding full literature reviews and focusing only on the marginal contribution.

A comprehensive pre-submission checklist designed for authors aiming to submit original research papers to high-level academic journals, such as Acta Electronica Sinica (AES). This guide covers essential academic components, including proper abstract structuring, keyword adherence, specialized referencing formats (GB/T 7714), blind review compliance, ethical data handling, and formatting standards. It minimizes the risk of rejection during the initial editorial screening process.

This guide outlines the stringent reproducibility and data requirements for submitting manuscripts to Agricultural Systems (AgSy). Because AgSy treats code, models, and algorithms as research data, authors must learn how to deposit all materials in a recognized repository (e.g., Zenodo, OSF), cite them permanently, and document the model structure, parameters, and versions. It provides a checklist for building a reproducible research workflow.

A comprehensive guide detailing how to package all necessary supplementary evidence for academic submissions in AI, statistics, and machine learning domains. It covers best practices for ensuring reproducibility, including managing proofs, simulation scripts, benchmark data, random seeds, and minimizing metadata risks for reviewers.

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 guide details the critical steps required after conference acceptance to prepare a paper for the final, public 'camera-ready' submission (e.g., AISTATS, PMLR). It covers transforming an anonymous manuscript into the final two-column format, restoring author metadata, handling public artifacts (code, data), resolving formatting issues, and confirming all presentation and copyright obligations.

This guide provides strict guidelines for designing, auditing, and reporting empirical or simulation experiments, particularly for theoretical machine learning and statistics papers. It emphasizes mapping every empirical claim to specific evidence, ensuring statistical rigor (e.g., reporting uncertainty, baselines, seeds), and validating theoretical assumptions rather than optimizing for leaderboards. Essential for research reproducibility.

This comprehensive guide helps researchers position their academic paper for AISTATS, ensuring maximum novelty and eligibility. It provides detailed advice on comparative analysis, teaching authors how to contrast statistical methods against both modern ML literature (e.g., NeurIPS, ICML) and established statistical theory (e.g., JASA, Annals of Statistics). It covers proper citation of concurrent work, understanding archival status, and structuring the 'Related Work' section to highlight unique technical contributions.

A comprehensive guide for academics submitting AI and statistical research papers. It details how to map every claim (theoretical, algorithmic, or empirical) to verifiable evidence, covering necessary details like datasets, hyperparameters, random seeds, compute resources, and uncertainty estimates. It outlines the standards for achieving high levels of reproducibility, from descriptive to turnkey, ensuring scientific rigor for top-tier conferences.

This guide provides a comprehensive framework for understanding the academic peer review process used at AISTATS, covering everything from OpenReview submission workflows and author-reviewer dynamics to advanced decision criteria (correctness, significance, empirical support). It helps researchers strategize their submissions and responses to maximize acceptance chances in statistics and machine learning.

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.