69 citations · 261 across the 20 of their papers we have counts for
9 papers · 1 filter
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…
Ideal Bayesian Spatial Adaptation
Veronika Rockova, Judith Rousseau
Many real-life applications involve estimation of curves that exhibit complicated shapes including jumps or varying-frequency oscillations. Practical methods have been devised that…
Metropolis-Hastings via Classification
Tetsuya Kaji, Veronika Rockova
This paper develops a Bayesian computational platform at the interface between posterior sampling and optimization in models whose marginal likelihoods are difficult to evaluate. I…
The art of BART: Minimax optimality over nonhomogeneous smoothness in high dimension
Seonghyun Jeong, Veronika Rockova
Many asymptotically minimax procedures for function estimation often rely on somewhat arbitrary and restrictive assumptions such as isotropy or spatial homogeneity. This work enhan…
Uncertainty Quantification for Sparse Deep Learning
Yuexi Wang, Veronika Ročková
Deep learning methods continue to have a decided impact on machine learning, both in theory and in practice. Statistical theoretical developments have been mostly concerned with ap…
Uncertainty Quantification for Bayesian CART
Ismael Castillo, Veronika Rockova
This work affords new insights into Bayesian CART in the context of structured wavelet shrinkage. The main thrust is to develop a formal inferential framework for Bayesian tree-bas…
On Semi-parametric Bernstein-von Mises Theorems for BART
Veronika Rockova
Few methods in Bayesian non-parametric statistics/ machine learning have received as much attention as Bayesian Additive Regression Trees (BART). While BART is now routinely perfor…
The Median Probability Model and Correlated Variables
Marilena Barbieri, James O. Berger, Edward I. George +1
The median probability model (MPM) Barbieri and Berger (2004) is defined as the model consisting of those variables whose marginal posterior probability of inclusion is at least 0.…
Bayesian Dyadic Trees and Histograms for Regression
Stephanie van der Pas, Veronika Rockova
Many machine learning tools for regression are based on recursive partitioning of the covariate space into smaller regions, where the regression function can be estimated locally.…