Skill UI

This guide provides a comprehensive framework for authors targeting elite AI/ML conferences like ICML. It covers academic positioning, identifying core ML contributions, structuring the narrative (Problem, Why Now, What is New), and establishing a rigorous evidence bar, including baselines and ablations. It also advises on distinguishing the work from closely related venues (e.g., NeurIPS, ICLR).

A comprehensive guide for authors targeting the International Conference on Pattern Recognition (ICPR). This skill helps evaluate if a manuscript fits the venue's scope, covering core topics like pattern recognition, computer vision, biometrics, and machine learning. It details necessary framing, evidence requirements (e.g., ablations, limitations), and how to differentiate the work from similar top-tier conferences (e.g., CVPR, ICML).

A comprehensive guide for authors targeting the International Journal of Machine Tools and Manufacture (IJMTM). This skill helps assess the fit of a manuscript, ensuring the work elevates beyond simple parameter optimization to provide a deep, mechanistic, and quantitative understanding of manufacturing processes, machine-tool dynamics, and metrology. Essential for framing high-impact scientific contributions.

This guide provides a structured protocol for authors writing methods papers in statistics or econometrics. It teaches how to define the precise 'delta' (incremental contribution) of a new method by explicitly referencing 3-6 existing state-of-the-art techniques. It emphasizes positioning the work across both traditional econometric theory and modern machine learning/data science literature, ensuring the contribution is clearly stated against named prior methods, not as a general survey.

This comprehensive skill guides authors through the specific submission requirements and editorial culture of the Journal of Machine Learning Research (JMLR). It helps authors determine if their ML method, theory paper, or dataset is appropriate for JMLR's high standards—emphasizing rigor, complete proofs, open code, and lasting scientific contribution over novelty signaling. Use this when targeting JMLR or comparing it against other top-tier ML venues.

A comprehensive guide detailing how to structure, package, and validate research artifacts for major machine learning conferences like KDD. It covers best practices for creating anonymous, reproducible repositories, handling scale claims, and providing robust evidence for different research tracks (Research, ADS, Datasets).

A comprehensive guide for structuring the empirical section of high-level machine learning papers (e.g., KDD). It details the 'four-axis evidence plan'—Quality, Scale, Efficiency, and Mechanism—and emphasizes critical data hygiene practices. Learn how to prevent temporal leakage, structure ablations in a matrix format, and design post-launch measurements to ensure academic rigor and reproducibility.

This guide provides best practices for ensuring the scientific rigor and reproducibility of data mining and machine learning research published at top-tier conferences like KDD. It details a three-tier honesty model (Rerunnable, Rebuildable, Attested) and provides comprehensive checklists for disclosing pipeline stages, handling stochasticity, and documenting configurations to build reader trust.

A comprehensive guide for authors applying machine learning techniques to healthcare. It helps authors frame their research to meet the high standards of top ML4H conferences, covering biomedical data leakage, confounding effects, required evidence standards, and optimal submission strategies.

This comprehensive guide instructs researchers on how to design rigorous and credible evaluations for Machine Learning systems. It covers critical areas such as workload selection, baseline comparison discipline, reporting four key coordinates (throughput, latency tails, memory, cost), structuring ablations, and generating scaling evidence. It ensures measurements meet academic benchmarking standards.

Provides comprehensive guidelines for achieving high reproducibility in Machine Learning Systems (MLSys) performance claims. It details how to rigorously control sources of non-determinism (e.g., thermal noise, random seeds) and pin the entire technical stack—from hardware and drivers to frameworks and serving policies—to ensure results can be validated by third parties.

This guide helps researchers determine if their project genuinely involves machine learning and computer system co-design. It outlines criteria, such as the "co-design test," and provides a routing table to decide whether the work belongs at MLSys compared to general systems conferences (OSDI, SOSP) or pure ML venues (ICML, NeurIPS).