Guidelines for CPL reviewersPlease touch upon as many of the following points as practical:
Goals: What are research goals?
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 CPL? 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,
- Is it written in a clear, readable style, with good grammar and few (if any) typographical errors?
- Are the goals and contributions of the work clearly and correctly stated?
- Are the problem description, approach and evaluation adequately detailed for others to replicate the work?
- If the paper introduces new terminology or techniques, does it explain why current terminology or techniques are insufficient?
- Does it include examples?