4 accepted papers
We develop a multivariate posterior sampling procedure through deep generative quantile learning. Simulation proceeds implicitly through a push-forward mapping that can transform i.i.d. random vectors samples from the posterior. We utilize Monge-Kantorovich depth in multivariate quantiles to directl…
There has been a growing realization of the potential of Bayesian machine learning as a platform that can provide both flexible modeling, accurate predictions as well as coherent uncertainty statements. In particular, Bayesian Additive Regression Trees (BART) have emerged as one of today’s most effe…
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 approximability or rates of estimation when recovering infinite dimensional objects (curves or densities). Despite the imp…
Recent years have seen growing interest in deterministic search approaches to spike-and-slab Bayesian variable selection. Such methods have focused on the goal of finding a global mode to identify a “best model”. However, the report of a single model will be a misleading reflection of the model unce…