Skill UI

This guide details best practices for achieving reproducibility in highly theoretical computer science papers, such as those submitted to ICALP. Since artifact evaluation is absent, reproducibility hinges on the ability for any reader to independently verify every step of the proof. Key requirements include writing self-contained proofs, providing verifiable computational certificates, maintaining consistency between submissions, and optionally using formal verification tools like Coq or Lean.

A comprehensive guide for researchers submitting signal processing papers, detailing how to achieve rigorous reproducibility for conferences like ICASSP. It emphasizes that simply providing a model is insufficient; authors must pin down the 'scoring ruler,' data versions, front-end parameters, and computational seeds to ensure every reported metric can be independently verified.

A comprehensive guide detailing the critical workflow from paper acceptance to final camera-ready submission for major computational conferences (like ICCV). It covers technical requirements such as de-anonymization, synchronizing multiple public copies (arXiv, CVF, IEEE), updating URLs, and managing logistical details for presentation.

This guide provides comprehensive standards for designing, auditing, and documenting AI experiments for major conferences like IJCAI. It covers establishing strong baselines, conducting ablation studies, and reporting meticulous details on hyperparameters, computational infrastructure, and ethical risks, ensuring maximum reproducibility and academic credibility for AI research.

This skill guides authors in refining the core computational or methodological claim of a technical manuscript, especially for journals like IJOC. It teaches how to move beyond merely describing an application and instead rigorously defining what is computationally new, by how much, and against which baseline. It structures the primary contribution sentence to be precise, falsifiable, and method-focused.

This comprehensive guide provides best practices for designing, running, and reporting computational experiments for academic journals. It teaches authors how to make performance claims scientifically defensible by addressing potential referee criticisms (e.g., tuning artifacts, seed luck). It covers advanced statistical techniques (performance profiles, nonparametric tests) and details the full structure required for a complete, verifiable GitHub data deposit.

A comprehensive guide for authors on how to position their computational or methodological contributions when submitting to the INFORMS Journal on Computing (IJOC). It emphasizes the critical need to advance against two distinct frontiers simultaneously: the Operations Research/Mathematical Science problem domain, and the core computing methodology. Use this when reviewer feedback questions the novelty or when your work straddles OR and CS boundaries.

A comprehensive guide to designing rigorous, reproducible, and fair experimental protocols for computational science research, particularly for top-tier academic journals. It covers critical elements such as selecting standard benchmark instances, establishing strong baselines, ensuring fair parameter tuning, reporting hardware constraints, and performing robust statistical validation, helping researchers avoid common pitfalls and maximize the scientific validity of their claims.

A comprehensive guide for drafting professional and evidence-based rebuttal letters following major or minor revisions in high-level computing journals. This skill helps authors structure point-by-point responses, address computational objections with re-run evidence, and strategically manage disagreements while maintaining a professional tone.

This guide provides best practices for creating clear and legible computational exhibits for academic manuscripts, particularly for INFORMS journals. It covers advanced techniques like performance profiling, visualizing scaling behavior on log axes, and presenting stochastic results with full dispersion. The goal is to ensure that the presented figures and tables strongly support the computational claims while maintaining rigorous methodological standards and reproducibility.

This comprehensive guide outlines the necessary theoretical rigor for algorithms and models submitted to top-tier computational journals. It instructs authors on how to formally state mathematical formulations, rigorously prove model validity, establish convergence, and quantify complexity (e.g., approximation ratios, finite termination). It provides specialized paths for exact methods, metaheuristics, and ML-enhanced OR, ensuring that the theoretical advancements fully support and rationalize the experimental findings.

This guide helps researchers determine if their Operations Research (OR) or analytics findings are sufficiently computation-first for submission to the INFORMS Journal on Computing (IJOC). It provides a rigorous scope test, focusing on computational/methodological advances, measurable performance claims, and reproducibility standards, ensuring authors select the correct technical area and venue.