Not all Jensen-Shannon Divergence Estimators are Equal
The Jensen-Shannon divergence is widely reported as a scalar measure of fidelity for synthetic tabular data. Yet, in practice, it is estimated from finite samples using protocols that are often underspecified. This creates a measurement problem. Although the population divergence is well defined, the empirical value depends on the estimator family, sampling protocol, calibration, dimensionality, and class balance. We show that different protocols can yield non-comparable values: marginal-based estimators ignore dependencies in the joint distribution and can severely underestimate divergence, while classifier-based estimators capture joint structure but exhibit strong estimator dependence....
| Authors | Alba Garrido, Alejandro Almodóvar, Mar Elizo et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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