Representation Preserving Multiclass Agnostic to Realizable Reduction

This work demonstrates a simple and elegant agnostic to realizable reduction for multiclass classification within the PAC learning framework, and explores representation preserving reductions to the realizable setting for two noise models in the multiclass PAC learning framework.