13 citations · 37 across the 13 of their papers we have counts for
22 papers
What Functions Does XGBoost Learn?
Dohyeong Ki, Adityanand Guntuboyina
This paper establishes a rigorous theoretical foundation for the function class implicitly learned by XGBoost, bridging the gap between its empirical success and our theoretical un…
Totally Concave Regression
Dohyeong Ki, Adityanand Guntuboyina
Shape constraints in nonparametric regression provide a powerful framework for estimating regression functions under realistic assumptions without tuning parameters. However, most…
Convergence rates for estimating multivariate scale mixtures of uniform densities
Arlene K. H. Kim, Gil Kur, Adityanand Guntuboyina
The Grenander estimator is a well-studied procedure for univariate nonparametric density estimation. It is usually defined as the Maximum Likelihood Estimator (MLE) over the class…
MARS via LASSO
Dohyeong Ki, Billy Fang, Adityanand Guntuboyina
Multivariate adaptive regression splines (MARS) is a popular method for nonparametric regression introduced by Friedman in 1991. MARS fits simple nonlinear and non-additive functio…
Multivariate, Heteroscedastic Empirical Bayes via Nonparametric Maximum Likelihood
Jake A. Soloff, Adityanand Guntuboyina, Bodhisattva Sen
Multivariate, heteroscedastic errors complicate statistical inference in many large-scale denoising problems. Empirical Bayes is attractive in such settings, but standard parametri…
A Nonparametric Maximum Likelihood Approach to Mixture of Regression
Hansheng Jiang, Adityanand Guntuboyina
We study mixture of linear regression (random coefficient) models, which capture population heterogeneity by allowing the regression coefficients to follow an unknown distribution…