JMLR Scope

JMLR seeks previously unpublished papers on machine learning that contain:

new algorithms with empirical, theoretical, psychological, or biological justification;
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.