2 citations · 4 across the 12 of their papers we have counts for
13 papers
Private Generative Bootstrap via Blocking
Jinwon Sohn, Veronika Ročková
With AI systems gaining more access to individuals' information, it is important to protect privacy when reporting statistical answers. Equally important is to privatize the report…
Jigsaw Regularization in Whole-Slide Image Classification
So Won Jeong, Veronika Ročková
Computational pathology involves the digitization of stained tissues into whole-slide images (WSIs) that contain billions of pixels arranged as contiguous patches. Statistical anal…
Generative Bayesian Filtering and Parameter Learning
Edoardo Marcelli, Sean O'Hagan, Veronika Rockova
Generative Bayesian Filtering (GBF) provides a powerful and flexible framework for performing posterior inference in complex nonlinear and non-Gaussian state-space models. Our appr…
Conditional Flow Matching for Bayesian Posterior Inference
Percy S. Zhai, So Won Jeong, Veronika Ročková
We propose a generative multivariate posterior sampler via flow matching. It offers a simple training objective, and does not require access to likelihood evaluation. The method le…
From Small to Large Language Models: Revisiting the Federalist Papers
So Won Jeong, Veronika Ročková
For a long time, the authorship of the Federalist Papers had been a subject of inquiry and debate, not only by linguists and historians but also by statisticians. In what was argua…
AI-Powered Bayesian Inference
Sean O'Hagan, Veronika Ročková
The advent of Generative Artificial Intelligence (GAI) has heralded an inflection point that changed how society thinks about knowledge acquisition. While GAI cannot be fully trust…
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…
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,…
Adaptive Bayesian Predictive Inference in High-dimensional Regerssion
Veronika Rockova
Bayesian predictive inference provides a coherent description of entire predictive uncertainty through predictive distributions. We examine several widely used sparsity priors from…
Generative Bayesian Inference with GANs
Yuexi Wang, Veronika Ročková
In the absence of explicit or tractable likelihoods, Bayesians often resort to approximate Bayesian computation (ABC) for inference. Our work bridges ABC with deep neural implicit…
Posterior Concentration for Bayesian Regression Trees and Forests
Veronika Rockova, Stephanie van der Pas
Since their inception in the 1980's, regression trees have been one of the more widely used non-parametric prediction methods. Tree-structured methods yield a histogram reconstruct…