Skill UI

A comprehensive guide designed to help researchers determine the optimal conference or journal venue (e.g., AAMAS, NeurIPS, ICML, EC) for multi-agent AI research. It emphasizes that the contribution must center on the interaction result—such as mechanism design, coordination, or negotiation—rather than simply applying a single-agent method in a multiagent testbed. This tool sharpens the research framing before writing begins.

A comprehensive guide for authors aiming to submit machine learning manuscripts to the Asian Conference on Machine Learning (ACML). This skill helps evaluate manuscript fit, re-frame research from journal or arXiv styles, understand required contribution types (e.g., algorithms, theorems, benchmarks), and align the paper's evidence bar with the expectations of a top-tier, regional AI/ML venue. Use it for venue selection and submission strategy development.

This guide helps researchers determine if their embodied AI or robotics project is best suited for the Conference on Robot Learning (CoRL). It provides a rigorous two-axis test—assessing if learning is the core contribution and if embodiment is essential to the claim—to decide between submitting to CoRL, ICRA, IROS, NeurIPS, or other journals.

A comprehensive guide for computer vision researchers on strategically positioning their work. It helps assess whether the core contribution is related to visual data or visual computation, providing fit tests and a routing map to recommend the most appropriate top-tier conferences (CVPR, ICCV, NeurIPS) or journals based on the contribution type and technical maturity.

This guide assists researchers in determining the optimal venue for their theoretical machine learning contributions. It provides a framework for evaluating whether a result is best suited for COLT, or if it belongs in other top-tier conferences (e.g., NeurIPS, ICML) or journals (e.g., JMLR). It emphasizes differentiating between rigorous mathematical proofs (bounds, separations) and purely empirical findings.

A comprehensive guide for researchers deciding the best venue for their machine learning manuscript. It helps evaluate whether a paper is a strong fit for ICML, or if it should be rerouted to specialized tracks, other top-tier conferences (NeurIPS, ICLR), or academic journals based on its core contribution type (theory, method, application) and novelty.

This guide assists researchers in determining the topic suitability, appropriate format (research paper, review, special issue), and correct academic placement for manuscripts targeting 'Pattern Recognition and Artificial Intelligence' (PR&AI). It outlines the journal's focused scope across core AI domains like ML, CV, and NLP, helping authors avoid common submission pitfalls and ensuring their contribution has sufficient originality and academic depth.