Mathematical Foundations of Machine Learning

Mathematical Foundations of Machine Learning (MFML) is a forum for the publication of highest-quality peer-reviewed papers on the broad mathematical foundations of machine learning.

-Encourages papers concerned with all pure and applied mathematical aspects of machine learning, with a particular focus on foundational work.
-Explicitly encourages papers of conceptual value residing in the synthesis of mathematical theories and their application to fundamental problems in machine learning theory and practice.
-All papers must be characterised by originality and mathematical rigour; survey papers are welcome as well and must exhibit originality in the synthesis of the material and their vantage point.
-For a paper to be accepted, it is not enough that it contain original results. In fact, results should be highly relevant to the mathematical foundations of machine learning with a wide readership in mind.