14 citations · 27 across the 6 of their papers we have counts for
13 papers
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…
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…
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…
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…
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…