Skill UI

A detailed guide for authors submitting theoretical machine learning papers (like COLT) regarding the appropriate inclusion and design of numerical simulations. It emphasizes that numerics should only visualize proved bounds, phase transitions, or separations, maintaining the focus on theoretical proofs rather than empirical data. Provides rules for plotting, reporting variability, and maintaining academic honesty.

A detailed guide for authors structuring the 'Related Work' section of theoretical machine learning papers. It shifts focus from qualitative prose to quantitative comparison, instructing users to build explicit tables comparing prior bounds versus new bounds, and systematically covering diverse literature lanes like complexity theory, statistics, and open problems to rigorously establish novelty.

This guide outlines the stringent standards for academic paper reproducibility in theoretical machine learning (e.g., COLT). It provides a comprehensive pre-submission audit checklist covering formal proof completeness, assumption bookkeeping, constant tracking, and ensuring numerical illustrations are fully traceable. Use this methodology to strengthen your theoretical claims and withstand rigorous peer review.

This guide details the rigorous, mathematically focused peer-review pipeline for top ML conferences like COLT (Conference on Learning Theory). It explains the double-anonymous review system, how reviewers assess correctness, significance, and novelty, and provides actionable advice on structuring rebuttals and interpreting final decisions for researchers aiming for top-tier academic publication.

This skill guides authors in assessing the suitability and optimal framing of their machine learning research in the health domain for top-tier academic conferences like CHIL. It ensures the manuscript meets the specific evidence standards, emphasizing clinical grounding, fairness, robustness, and deployment constraints, transforming general CS narratives into specialized health ML contributions.

This guide helps authors position their machine learning systems manuscripts for the rigorous Conference on Machine Learning and Systems (MLSys). It details the venue's specific focus on system-level contributions, including training/inference infrastructure, compilers, data systems, and hardware-aware ML. Learn how to frame your work to appeal to systems reviewers by emphasizing measurable impacts on capability, cost, reliability, or deployment scale.

This comprehensive style guide is written for authors submitting research papers to the CoRL conference. It provides specific directives on structuring the narrative to satisfy two distinct audiences: machine learning theorists and robotics engineers. It emphasizes quantifying all claims with concrete evidence scales (e.g., success rates, frequency), structuring the argument within a limited page count, and properly integrating mandatory sections like Limitations.

This guide provides comprehensive instructions for creating a robust, reproducible artifact package for database systems research. It details best practices including containerization, pinning workloads, generating a permanent DOI-archived record, and designing the package to be successfully built and demonstrated on a reviewer's clean machine within a tight time budget. The goal is to strengthen scientific rigor and ensure permanent, open-access citation.

A comprehensive guide for advanced econometric techniques in causal inference, covering modern approaches for quasi-experimental designs such as staggered Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Double Machine Learning (DML). It details best practices, critical diagnostic tests (e.g., weak instruments, parallel trend checks, bias decomposition), and robust reporting standards required for high-impact economic research.

This skill provides a strategic guide for authors evaluating if their AI/data mining manuscript is a suitable fit for the ECML PKDD conference. It covers scope definition, evidence requirements, academic framing, and strategic positioning relative to major global venues (e.g., ICML, NeurIPS). Use this to refine your submission narrative and maximize your chances of acceptance.

This guide meticulously models the rigorous decision-making machinery of top-tier academic conferences, using EuroSys as a detailed example. It covers the two-round submission cycle, the 'Accept/Revise/Reject' outcome logic, double-blind review mechanics, and critical criteria (like deployment plausibility and verifiable fairness) that determine a paper's success. Ideal for researchers and graduate students preparing systems research submissions.

This guide details best practices for submitting research artifacts to academic conferences, following the ACM Artifact Review and Badging scheme. It covers everything from required packaging plans (Docker, README, data provenance) to achieving various levels of validation (Functional, Reusable, Reproduced). Learn how to structure your submission to ensure maximum reproducibility and pass rigorous evaluator checks on a clean machine.