4 papers
Importance sampling for Bayesian inference: polynomial-dimension dependent error bounds
Fabián González, Víctor Elvira, Joaquín Míguez
Many Bayesian inference problems involve high-dimensional models where the performance of standard importance sampling (IS) methods often degrades rapidly as the dimensionality inc…
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…
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…
Training Implicit Generative Models via an Invariant Statistical Loss
José Manuel de Frutos, Pablo M. Olmos, Manuel A. Vázquez +1
Implicit generative models have the capability to learn arbitrary complex data distributions. On the downside, training requires telling apart real data from artificially-generated…