Experiments with conference processes

NIPS is a premier conference in machine learning (arguably the best, or co-best with ICML). NIPS has also been a source of interesting and ongoing experiments with the process of reviewing.

For example, in 2010 Rich Zemel, who was a PC chair of NIPS at the time, experimented with a new system he and Laurent Charlin were developing that would determine the "fit" between a potential reviewer and a submitted paper. This system, called the Toronto Paper Matching System, is now being used regularly in the ML/vision communities.

This year, NIPS is trying another experiment. In brief,

10% of the papers submitted to NIPS were duplicated and reviewed by two independent groups of reviewers and Area Chairs.

And the goal is to determine how inconsistent the reviews are, as part of a larger effort to measure the variability in reviewing. There's even a prediction market set up to guess what the degree of inconsistency will be. Also see Neil Lawrence's fascinating blog describing the mechanics of constructing this year's PC and handling the review process.

I quite like the idea of 'data driven' experiments with format changes. It's a pity that we didn't have a way of measuring the effect of having a two-tier committee for STOC a few years ago, and instead had to rely on anecdotes about its effectiveness, and didn't even run the experiment long enough to collect enough data. I feel that every time there are proposals to change anything about the theory conference process, the discussion gets drowned out in a din of protests, irrelevant anecdotes, and opinions based entirely on ... nothing..., and nothing ever changes.

Maybe there's something about worst-case analysis (and thinking) that makes change hard :).