3 papers
stat.ML2026
Approximating -Divergences with Rank Statistics
Viktor Stein, José Manuel de Frutos
We introduce a rank-statistic approximation of -divergences that avoids explicit density-ratio estimation by working directly with the distribution of ranks. For a resolution pa…
cs.LG2025
Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence
José Manuel de Frutos, Manuel A. Vázquez, Pablo M. Olmos +1
Rank-based statistical metrics, such as the invariant statistical loss (ISL), have recently emerged as robust and practically effective tools for training implicit generative model…
cs.LG2025
Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss
José Manuel de Frutos, Manuel A. Vázquez, Pablo Olmos +1
Traditional implicit generative models are capable of learning highly complex data distributions. However, their training involves distinguishing real data from synthetically gener…