95 citations · 168 across the 22 of their papers we have counts for
3 papers · 1 filter
Generative Bayesian Hyperparameter Tuning
Hedibert Lopes, Nick Polson, Vadim Sokolov
\noindent Hyper-parameter selection is a central practical problem in modern machine learning, governing regularization strength, model capacity, and robustness choices. Cross-vali…
Lower-dimensional posterior density and cluster summaries for overparameterized Bayesian models
Henrique Bolfarine, Hedibert F. Lopes, Carlos M. Carvalho
The usefulness of Bayesian models for density and cluster estimation is well established across multiple literatures. However, there is still a known tension between the use of sim…
Minnesota BART
Pedro A. Lima, Carlos M. Carvalho, Hedibert F. Lopes +1
Vector autoregression (VAR) models are widely used for forecasting and macroeconomic analysis, yet they remain limited by their reliance on a linear parameterization. Recent resear…