1 citations · 2 across the 4 of their papers we have counts for
6 papers
A Framework for an Assessment of the Kernel-target Alignment in Tree Ensemble Kernel Learning
Dai Feng, Richard Baumgartner
Kernels ensuing from tree ensembles such as random forest (RF) or gradient boosted trees (GBT), when used for kernel learning, have been shown to be competitive to their respective…
Nonparametric Analysis of Delayed Treatment Effects using Single-Crossing Constraints
Nicholas C. Henderson, Kijoeng Nam, Dai Feng
Clinical trials involving novel immuno-oncology (IO) therapies frequently exhibit survival profiles which violate the proportional hazards assumption due to a delay in treatment ef…
BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values
Dai Feng, Lili Zhao
There has been increasing interest in modeling survival data using deep learning methods in medical research. In this paper, we proposed a Bayesian hierarchical deep neural network…
(Decision and regression) tree ensemble based kernels for regression and classification
Dai Feng, Richard Baumgartner
Tree based ensembles such as Breiman's random forest (RF) and Gradient Boosted Trees (GBT) can be interpreted as implicit kernel generators, where the ensuing proximity matrix repr…
Random Forest (RF) Kernel for Regression, Classification and Survival
Dai Feng, Richard Baumgartner
Breiman's random forest (RF) can be interpreted as an implicit kernel generator,where the ensuing proximity matrix represents the data-driven RF kernel. Kernel perspective on the R…
DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values
Lili Zhao, Dai Feng
There has been increasing interest in modelling survival data using deep learning methods in medical research. Current approaches have focused on designing special cost functions t…