2 citations · 6 across the 8 of their papers we have counts for
8 papers
DANCE: Doubly Adaptive Neighborhood Conformal Estimation
Brandon R. Feng, Brian J. Reich, Daniel Beaglehole +7
The recent developments of complex deep learning models have led to unprecedented ability to accurately predict across multiple data representation types. Conformal prediction for…
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…
Deep Bayes Factors
Jungeum Kim, Veronika Rockova
The is no other model or hypothesis verification tool in Bayesian statistics that is as widely used as the Bayes factor. We focus on generative models that are likelihood-free and,…
On Mixing Rates for Bayesian CART
Jungeum Kim, Veronika Rockova
The success of Bayesian inference with MCMC depends critically on Markov chains rapidly reaching the posterior distribution. Despite the plentitude of inferential theory for poster…
Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classification
Jungeum Kim, Xiao Wang
The idea of robustness is central and critical to modern statistical analysis. However, despite the recent advances of deep neural networks (DNNs), many studies have shown that DNN…