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AISTATS Submission Topic Selection Guide

aistats-topic-selection

brycewang-stanford/Awesome-Journal-Skills

This guide helps researchers assess the optimal venue for their work, determining if their project aligns best with AISTATS, or if it is better suited for major ML conferences (NeurIPS, ICML, ICLR), UAI, or specific statistics journals. It emphasizes the need for strong statistical foundations, inference guarantees, and the unique intersection of AI, ML, and statistics before starting the writing process.

Guide to Language Model Conference Submission

colm-topic-selection

brycewang-stanford/Awesome-Journal-Skills

This skill helps authors strategically decide the optimal venue for their Language Model research. It provides the critical "object-of-study test" to determine if the LM itself is the subject of the study, or merely a tool for a task. Furthermore, it guides routing against top-tier NLP/AI conferences (ACL, NeurIPS, ICLR, etc.), helping authors target the right audience and maximizing submission chances.

CoRL Submission and Topic Routing Guide

corl-topic-selection

brycewang-stanford/Awesome-Journal-Skills

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.

AI and CS Conference Workflow Router

cs-ai-conference-workflow

brycewang-stanford/Awesome-Journal-Skills

A specialized routing tool designed for academic researchers submitting manuscripts to top-tier Computer Science and AI conferences. It helps users navigate complex decision points, such as selecting the best venue (e.g., ICML vs NeurIPS), matching contribution types (theory, system, empirical study), and adhering to specific submission guidelines and review constraints.

CVPR Research Venue Selection Guide

cvpr-topic-selection

brycewang-stanford/Awesome-Journal-Skills

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.

European ML Conference Submission Guide

european-conference-on-machine-learning-and-principles-and-practice-of-knowledge-discovery

brycewang-stanford/Awesome-Journal-Skills

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.

Guiding ML Theory Venue Selection

colt-topic-selection

brycewang-stanford/Awesome-Journal-Skills

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.

Selecting ML Conference Venue: ICLR Guide

iclr-topic-selection

brycewang-stanford/Awesome-Journal-Skills

This guide helps researchers determine if their Machine Learning project is suitable for ICLR, or if a different top-tier conference (such as NeurIPS, ICML, or CVPR) is a better fit. It emphasizes the need for deep representation-learning insights, mechanistic understanding, and theoretical contributions over mere application performance.

Positioning Novelty in ML Research Papers

icml-related-work

brycewang-stanford/Awesome-Journal-Skills

This guide provides structured methodologies for authors to position their research within the context of existing literature and concurrent submissions at top-tier machine learning conferences (e.g., ICML, NeurIPS). It emphasizes identifying the technical delta, addressing novelty concerns, and properly citing related work to maximize acceptance chances.

ML Paper Submission Topic Selection Guide

icml-topic-selection

brycewang-stanford/Awesome-Journal-Skills

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.

Selecting the Best AI Conference Venue

ijcai-topic-selection

brycewang-stanford/Awesome-Journal-Skills

A comprehensive framework for researchers to assess if their AI research contribution is best suited for a major conference like IJCAI, or if a highly specialized venue (such as NeurIPS, ICML, CVPR, or ACL) would provide a clearer audience. It guides decisions based on technical depth, problem scope, and contribution type.

Positioning ML Papers for Top Conferences

international-conference-on-machine-learning

brycewang-stanford/Awesome-Journal-Skills

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).

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