Guidelines for JMLR reviewers

Please touch upon as many of the following points as practical:

Goals: What are research goals and learning task?

Description: Is the description adequately detailed for others to replicate the work? Is it clearly written in good style and does it include examples? Papers describing systems should clearly describe the contributions or the principles underlying the system. Papers describing theoretical results should also discuss their practical utility.

Evaluation: Do the authors evaluate their work in an adequate way (theoretically and/or empirically)? Are all claims clearly articulated and supported either by empirical experiments or theoretical analyses? If appropriate, have the authors implemented their work and demonstrated its utility on a significant problem?

Significance: Does the paper constitute a significant, technically correct contribution to the field that is appropriate for JMLR? Is it sufficiently different from prior published work (by the author or others) to merit a new publication? Is it clear how the work advances the current state of understanding, and why the advance matters?

Related Work and Discussion: Are strength and limitations and generality of the research adequately discussed, in particular in relation to related work? Do the authors clearly acknowledge and identify the contributions of their predecessors?

Clarity: Is it written in a way such that an interested reader with a background in machine learning, but no special knowledge of the paper's subject, could understand and appreciate the paper's results? In particular,

Recommendation: Please also recommend a decision: accept, conditional accept, reject with encouragement to revise and resubmit, and reject.  If you suggest conditional accept, please provide a precise list of changes that can easily be checked upon resubmission.

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