  In this paper we study the problem of multiclass classification with a
bounded number of different labels $k$, in the realizable setting. We extend
the traditional PAC model to a) distribution-dependent learning rates, and b)
learning rates under data-dependent assumptions. First, we consider the
universal learning setting (Bousquet, Hanneke, Moran, van Handel and
Yehudayoff, STOC '21), for which we provide a complete characterization of the
achievable learning rates that holds for every fixed distribution. In
particular, we show the following trichotomy: for any concept class, the
optimal learning rate is either exponential, linear or arbitrarily slow.
Additionally, we provide complexity measures of the underlying hypothesis class
that characterize when these rates occur. Second, we consider the problem of
multiclass classification with structured data (such as data lying on a low
dimensional manifold or satisfying margin conditions), a setting which is
captured by partial concept classes (Alon, Hanneke, Holzman and Moran, FOCS
'21). Partial concepts are functions that can be undefined in certain parts of
the input space. We extend the traditional PAC learnability of total concept
classes to partial concept classes in the multiclass setting and investigate
differences between partial and total concepts.
