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journal-of-machine-learning-research

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Use when targeting Journal of Machine Learning Research (JMLR) or deciding whether a machine learning methods or theory manuscript fits this open-access venue. Encodes the journal's fit, framing, method-and-evidence bar, house style, official-submission re-check, and desk-reject heuristics.

When to use

• targeting Journal of Machine Learning Research (JMLR)
• deciding whether a machine learning methods or theory manuscript fits JMLR
• assessing fit versus TPAMI, NeurIPS journal track, or Nature Machine Intelligence
• understanding JMLR's editorial culture and rejection profile
• rigorous theoretical analysis, new algorithmic framework, or well-grounded empirical methodology
• machine learning algorithms with rigorous analysis or strong theoretical grounding
• statistical learning theory, optimization for machine learning, probabilistic graphical models
• significant open-source software or benchmark datasets for the ML community

When not to use

• replacing the journal's current official submission guidelines
• fast-paced competitive benchmarking
• framing calibrated to leaderboard position
• conference proceedings replacement

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