AI RESEARCH
Structural Learning Theory: A Metric-Topology Factorization Approach
arXiv CS.LG
•
ArXi:2602.07974v2 Announce Type: replace Learning in structured, multi-context, or non-stationary environments involves two orthogonal difficulties. The first is \emph{metric}: once the correct context is known, how hard is prediction within it? This is the domain of Statistical Learning Theory (SLT). The second is \emph{structural}: how many local contexts are required, and how can they be discovered from data? This paper develops \emph{Structural Learning Theory} (StrLT) for the structural axis. We.