  This open problem asks whether there exists an online learning algorithm for
binary classification that guarantees, for all target concepts, to make a
sublinear number of mistakes, under only the assumption that the (possibly
random) sequence of points X allows that such a learning algorithm can exist
for that sequence. As a secondary problem, it also asks whether a specific
concise condition completely determines whether a given (possibly random)
sequence of points X admits the existence of online learning algorithms
guaranteeing a sublinear number of mistakes for all target concepts.
