JMLR seeks previously unpublished papers on machine learning that contain: •new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature;
/// experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems;
/// accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods;
/// formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks;
/// development of new analytical frameworks that advance theoretical studies of practical learning methods;
/// computational models of data from natural learning systems at the behavioral or neural level; or
/// extremely well-written surveys of existing work.