ICML Reviewing Criteria

Michael Littman and Leon Bottou have decided to use a franchise program chair approach to reviewing at ICML this year. I’ll be one of the area chairs, so I wanted to mention a few things if you are thinking about naming me.

  1. I take reviewing seriously. That means papers to be reviewed are read, the implications are considered, and decisions are only made after that. I do my best to be fair, and there are zero subjects that I consider categorical rejects. I don’t consider several arguments for rejection-not-on-the-merits reasonable.
  2. I am generally interested in papers that (a) analyze new models of machine learning, (b) provide new algorithms, and (c) show that they work empirically on plausibly real problems. If a paper has the trifecta, I’m particularly interested. With 2 out of 3, I might be interested. I often find papers with only one element harder to accept, including papers with just (a).
  3. I’m a bit tough. I rarely jump-up-and-down about a paper, because I believe that great progress is rarely made. I’m not very interested in new algorithms with the same theorems as older algorithms. I’m also cautious about new analysis for older algorithms, since I like to see analysis driving algorithm rather than vice-versa. I prioritize a proof-of-possibility over a quantitative improvement. I consider quantitative improvements of small constant factors in sample complexity significant. For computationaly complexity, I generally want to see at least an order of magnitude improvement. I generally disregard any experiments on toy data, because I’ve found that toy data and real data can too-easily differ in their behavior.
  4. My personal interests are pretty well covered by existing papers, but this is perhaps not too important a criteria, compared to the above, as I easily believe other subjects are interesting.