Lower Bounds for the Total Variation Distance Given Means and Variances of Distributions

This work addresses a fundamental statistical problem for researchers in probability theory and machine learning, offering incremental theoretical advances in bounding distribution distances.

The paper tackles the problem of bounding the total variation distance between probability distributions when only means and variances are known, providing lower bounds for arbitrary measures in d-dimensional space and a tight bound in the one-dimensional case.