The Geometry of Statistical Feature Learning in Mean-Field Langevin Dynamics
This work develops a geometric framework for statistical feature learning using spherical mean-field Langevin dynamics (MFLD). It shows that training naturally discovers a low-dimensional structure in features through a base–fiber decomposition, leading to optimal prediction rates and self-regularization. Rigorous probabilistic tools confirm these structural properties in the low-temperature regime.