Call for papers, Less is More

Submissions to this track must be situated in the SE literature and come with a cover letter stating that they wish to be reviewed as “less is more” paper.

This call is ongoing and we are looking for papers that; e.g.

•Re-examine existing results and demonstrate how task A can be better performed by a simpler method (e.g. smaller, faster, etc.).

•We are also interested in ablation studies that remove parts of an implementation until performance drops;

•Instance or feature selection methods to reduce the training set;

•Distillation methods to reduce the size of a learned model;

•Variance studies that show that the improvement of a complex method over a simpler one is insignificant;

•Studies that show that a 10% system can perform as well as a 100% system.

•Semi-supervised learning methods that let us do much more with much less data;