1 citations · 1 across the 4 of their papers we have counts for
5 papers
GRASP: Grouped Regression with Adaptive Shrinkage Priors
Shu Yu Tew, Daniel F. Schmidt, Mario Boley
We introduce GRASP, a simple Bayesian framework for regression with grouped predictors, built on the normal beta prime (NBP) prior. The NBP prior is an adaptive generalization of t…
Improving Random Forests by Smoothing
Ziyi Liu, Phuc Luong, Mario Boley +1
Random forest regression is a powerful non-parametric method that adapts to local data characteristics through data-driven partitioning, making it effective across diverse applicat…
Roadmap on Data-Centric Materials Science
Stefan Bauer, Peter Benner, Tristan Bereau +58
Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, h…
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery
Mario Boley, Felix Luong, Simon Teshuva +3
Materials discovery driven by statistical property models is an iterative decision process, during which an initial data collection is extended with new data proposed by a model-in…
Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization
Shu Yu Tew, Mario Boley, Daniel F. Schmidt
We present a novel method for tuning the regularization hyper-parameter, , of a ridge regression that is faster to compute than leave-one-out cross-validation (LOOCV) while yiel…