5 citations · 5 across the 2 of their papers we have counts for
2 papers
stat.ML2023
Taylor Learning
James Schmidt
Empirical risk minimization stands behind most optimization in supervised machine learning. Under this scheme, labeled data is used to approximate an expected cost (risk), and a le…
stat.ML2023★ 5 cited
Testing for Overfitting
James Schmidt
High complexity models are notorious in machine learning for overfitting, a phenomenon in which models well represent data but fail to generalize an underlying data generating proc…