Skill UI

A comprehensive guide for refining machine learning research papers tailored for NeurIPS submission. It teaches how to structure introductions, calibrate claims with evidence, explicitly state limitations, and address mandatory academic checklist items (e.g., reproducibility, broader impact). Use this skill to transform technical writing into a reviewer-proof, high-impact academic manuscript.

This guide provides comprehensive best practices for achieving scientific reproducibility in systems and programming language papers, following stringent SIGPLAN guidelines. It details the necessary rigor for documenting hardware, managing complex runtime measurements (like JIT warmup and variance), and maintaining a detailed, machine-readable provenance ledger and data-availability statement.

This comprehensive guide outlines the entire lifecycle of submitting and revising academic papers for major conferences like OOPSLA. It details strategic decisions, such as choosing between October or March submission rounds, managing the state machine (Accept, Minor, Major Revision, Reject), budgeting time for necessary revisions, and planning post-publication artifacts and talks. Essential for academic research teams.

This guide outlines the rigorous analytical methodology required for Operations Research (OR) manuscripts. It instructs users on selecting the appropriate mathematical machinery—such as duality, dynamic programming, convergence analysis, or concentration inequalities—to establish provable guarantees, approximation factors, and algorithmic complexity. It ensures that claims are backed by rigorous proofs, moving beyond mere heuristics.

Provides a rigorous framework for ensuring the reproducibility of systems research presented at conferences like OSDI. It mandates maintaining a comprehensive, machine-readable provenance ledger that records hardware specifications, software dependencies, workload traces, and measurement policies throughout the entire experimental lifecycle, preventing 'artifact drift' and ensuring verifiable results.

This guide outlines the necessary steps to ensure that parallel computing performance papers are scientifically reproducible. Since parallel results depend heavily on machine architecture, topology, and runtime conditions, proper documentation must cover everything from CPU/GPU specifications and interconnects to thread pinning policies, random seeds, and compiler flags. The focus is on proving the scaling trend, not just the absolute numbers, allowing results to hold true across different hardware platforms.

A comprehensive guide for planning the entire PPoPP conference submission lifecycle. It outlines the critical path, from initial draft creation, securing machine time, through author rebuttal, artifact evaluation, and final camera-ready submission. Use this to manage the timeline backward from the final conference dates.

This guide outlines comprehensive best practices for ensuring the reproducibility of Machine Learning and Pattern Recognition research. It covers fixing random seeds, locking hardware/software dependencies (Python, CUDA, etc.), managing data splits, tracking pre-trained model versions, and structuring the required artifact package for external review. It is essential for building trust in published scientific results.

This guide helps authors refine the core contribution sentence for Quantitative Economics (QE) manuscripts. It shifts the focus from complex methodology (machinery) to the clear, quantitative economic answer (the payoff). Learn to structure claims by linking a clear economic question, a measured quantity (estimate, counterfactual), and a broader policy lesson, while explicitly stating scope and uncertainty to meet high academic standards.

This guide provides rigorous best practices for developing and presenting theoretical economic models for top-tier journals. It emphasizes framing results with a clear economic payoff, maintaining minimal model setups, and ensuring that complex, full proofs are relegated to the online appendix. The goal is to ensure the primary economic insight remains highly visible and actionable, rather than being buried in algebraic machinery.

A detailed guide modeling the complex academic review machinery, using SIGIR as a primary example. It covers everything from per-track anonymity regimes and PC-member nomination duties to the critical scoring criteria: evaluating validity, fairness of comparison, and statistical rigor. Essential reading for authors preparing high-stakes research submissions.

A comprehensive guide for authors aiming to publish in top-tier Information Retrieval (IR) conferences like SIGIR. It teaches the specific, quantitative 'native register' required, moving beyond general machine learning boilerplate. Covers structuring the first page arc, calibrating vocabulary, and building table-centric arguments under strict page limits.