Skill UI

This resource guides authors on positioning their research for the International Conference on Automated Machine Learning (AutoML). It helps evaluate if a manuscript—especially those involving NAS, HPO, or meta-learning—is a suitable fit for the venue. The focus is on framing contributions that enhance the automated search process, robust methodologies, and system pipelines, rather than solely relying on final leaderboard scores.

A comprehensive guide for authors targeting the International Conference on Autonomous Agents and Multiagent Systems (AAMAS). This skill helps authors strategically position their work by focusing on core themes like autonomous agents, multiagent learning, game-theoretic interaction, and decentralized decision-making. It covers submission framing, evidence requirements, and risk assessment to ensure manuscripts align with the venue's technical focus.

A comprehensive guide for authors submitting AI/ML papers to the International Conference on Learning Representations (ICLR). It helps authors position the work, optimize framing, assess evidence strength, and ensure the manuscript adheres to the rigorous standards of deep learning and representation learning required for a top-tier, open-review conference.

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).

This skill provides a comprehensive strategic guide for authors targeting top-tier AI conferences like IJCAI. It assists in framing the manuscript's contribution, ensuring broad intellectual relevance across learning, reasoning, and planning, and structuring evidence to meet the expectations of cross-area AI specialists. Use it to optimize your submission strategy and compare it against neighboring venues (e.g., AAAI, ICLR).

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 skill provides a comprehensive framework for researchers to pressure-test their projects against the stringent standards of top educational psychology journals. It guides users to ensure their work is theoretically grounded, methodologically rigorous, and educationally consequential, focusing on learning mechanisms rather than simple program evaluation or test validation.

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.