69 citations · 261 across the 20 of their papers we have counts for
23 papers
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…
Deep Bootstrap for Bayesian Inference
Lizhen Nie, Veronika Rockova
For a Bayesian, the task to define the likelihood can be as perplexing as the task to define the prior. We focus on situations when the parameter of interest has been emancipated f…
Approximate Bayesian Computation via Classification
Yuexi Wang, Tetsuya Kaji, Veronika Ročková
Approximate Bayesian Computation (ABC) enables statistical inference in simulator-based models whose likelihoods are difficult to calculate but easy to simulate from. ABC construct…
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…
Bayesian Bootstrap Spike-and-Slab LASSO
Lizhen Nie, Veronika Ročková
The impracticality of posterior sampling has prevented the widespread adoption of spike-and-slab priors in high-dimensional applications. To alleviate the computational burden, opt…
Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO
Ray Bai, Veronika Rockova, Edward I. George
High-dimensional data sets have become ubiquitous in the past few decades, often with many more covariates than observations. In the frequentist setting, penalized likelihood metho…
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…
Variable Selection via Thompson Sampling
Yi Liu, Veronika Rockova
Thompson sampling is a heuristic algorithm for the multi-armed bandit problem which has a long tradition in machine learning. The algorithm has a Bayesian spirit in the sense that…
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…
Adaptive Bayesian SLOPE -- High-dimensional Model Selection with Missing Values
Wei Jiang, Malgorzata Bogdan, Julie Josse +3
We consider the problem of variable selection in high-dimensional settings with missing observations among the covariates. To address this relatively understudied problem, we propo…
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…
Dynamic Sparse Factor Analysis
Kenichiro McAlinn, Veronika Rockova, Enakshi Saha
Its conceptual appeal and effectiveness has made latent factor modeling an indispensable tool for multivariate analysis. Despite its popularity across many fields, there are outsta…
On Theory for BART
Veronika Rockova, Enakshi Saha
Ensemble learning is a statistical paradigm built on the premise that many weak learners can perform exceptionally well when deployed collectively. The BART method of Chipman et al…
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.…
Variable Selection with ABC Bayesian Forests
Yi Liu, Veronika Ročková, Yuexi Wang
Few problems in statistics are as perplexing as variable selection in the presence of very many redundant covariates. The variable selection problem is most familiar in parametric…
Posterior Concentration for Sparse Deep Learning
Nicholas Polson, Veronika Rockova
Spike-and-Slab Deep Learning (SS-DL) is a fully Bayesian alternative to Dropout for improving generalizability of deep ReLU networks. This new type of regularization enables provab…
Variance prior forms for high-dimensional Bayesian variable selection
Gemma E. Moran, Veronika Rockova, Edward I. George
Consider the problem of high dimensional variable selection for the Gaussian linear model when the unknown error variance is also of interest. In this paper, we show that the use o…
Simultaneous Variable and Covariance Selection with the Multivariate Spike-and-Slab Lasso
Sameer K. Deshpande, Veronika Rockova, Edward I. George
We propose a Bayesian procedure for simultaneous variable and covariance selection using continuous spike-and-slab priors in multivariate linear regression models where q possibly…
Dynamic Variable Selection with Spike-and-Slab Process Priors
Veronika Rockova, Kenichiro McAlinn
We address the problem of dynamic variable selection in time series regression with unknown residual variances, where the set of active predictors is allowed to evolve over time. T…
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.…
Mortality Rate Estimation and Standardization for Public Reporting: Medicare's Hospital Compare
E. I. George, V. Rockova, P. R. Rosenbaum +2
Bayesian models are increasing fit to large administrative data sets and then used to make individualized recommendations. For instance, Medicare's Hospital Compare webpage provide…