2 citations · 6 across the 8 of their papers we have counts for
4 papers · 1 filter
Deep Generative Quantile Bayes
Jungeum Kim, Percy S. Zhai, Veronika Ročková
We develop a multivariate posterior sampling procedure through deep generative quantile learning. Simulation proceeds implicitly through a push-forward mapping that can transform i…
Adaptive Uncertainty Quantification for Generative AI
Jungeum Kim, Sean O'Hagan, Veronika Rockova
This work is concerned with conformal prediction in contemporary applications (including generative AI) where a black-box model has been trained on data that are not accessible to…
Inductive Global and Local Manifold Approximation and Projection
Jungeum Kim, Xiao Wang
Nonlinear dimensional reduction with the manifold assumption, often called manifold learning, has proven its usefulness in a wide range of high-dimensional data analysis. The signi…
Tree Bandits for Generative Bayes
Sean O'Hagan, Jungeum Kim, Veronika Rockova
In generative models with obscured likelihood, Approximate Bayesian Computation (ABC) is often the tool of last resort for inference. However, ABC demands many prior parameter tria…