On power chi expansions of $f$-divergences

This work provides theoretical tools for analyzing f-divergences in statistics and machine learning, but it appears incremental as it builds on existing expansion methods.

The paper tackles the problem of deriving power chi expansions for f-divergences using Taylor expansions of smooth generators, resulting in closed-form formulas, bounded approximations, or analytic series expressions for these divergences.