2 citations · 4 across the 3 of their papers we have counts for
4 papers
Estimating Shapley Effects in Big-Data Emulation and Regression Settings using Bayesian Additive Regression Trees
Akira Horiguchi, Matthew T. Pratola
Shapley effects are a particularly interpretable approach to assessing how a function depends on its various inputs. The existing literature contains various estimators for this cl…
A tree perspective on stick-breaking models in covariate-dependent mixtures
Akira Horiguchi, Cliburn Chan, Li Ma
Stick-breaking (SB) processes are often adopted in Bayesian mixture models for generating mixing weights. When covariates influence the sizes of clusters, SB mixtures are particula…
Using BART to Perform Pareto Optimization and Quantify its Uncertainties
Akira Horiguchi, Thomas J. Santner, Ying Sun +1
Techniques to reduce the energy burden of an industrial ecosystem often require solving a multiobjective optimization problem. However, collecting experimental data can often be ei…
Assessing variable activity for Bayesian regression trees
Akira Horiguchi, Matthew T. Pratola, Thomas J. Santner
Bayesian Additive Regression Trees (BART) are non-parametric models that can capture complex exogenous variable effects. In any regression problem, it is often of interest to learn…