Upcoming events
9/11/2026
Friday 1:30-2:45pm,
Place: Genome Cafe, Room E3609
Light lunch 1:10-1:30pm
Zoom link: https://jh.zoom.us/j/99363808800
Title: Distributional Evaluation of Generative Models via Relative Density Ratio
Abstract:
We propose a function-valued evaluation metric for generative models based on the relative density ratio (RDR) designed to characterize distributional differences between real and generated samples. As an evaluation metric, the RDR function preserves $\phi$-divergence between two distributions, enables sample-level evaluation that facilitates downstream investigations of feature-specific distributional differences, and has a bounded range that affords clear interpretability and numerical stability. Function estimation of the RDR is achieved efficiently through optimization on the variational form of $\phi$-divergence. We provide theoretical convergence rate guarantees for general estimators based on M-estimator theory, as well as the convergence rate of neural network-based estimators when the true ratio is in the anisotropic Besov space. We demonstrate the power of the proposed RDR-based evaluation through numerical experiments on MNIST, CelebA64, and the American Gut project microbiome data. We show that the estimated RDR enables not only effective overall comparison of competing generative models, but also a convenient way to reveal the underlying nature of goodness-of-fit. This enables one to assess support overlap, coverage, and fidelity while pinpointing regions of the sample space where generators concentrate and revealing the features that drive the most salient distributional differences.
10/23/2026
Youngjoo Cho, Associate Professor, Department of Applied Statistics, Konkuk University
11/6/2026
Rajeshwari Sundaram, Senior Investigator, NICHD, NIH