5 citations · 6 across the 4 of their papers we have counts for
4 papers
Causal inference through multi-stage learning and doubly robust deep neural networks
Yuqian Zhang, Jelena Bradic
Deep neural networks (DNNs) have demonstrated remarkable empirical performance in large-scale supervised learning problems, particularly in scenarios where both the sample size …
Adaptive Split Balancing for Optimal Random Forest
Yuqian Zhang, Weijie Ji, Jelena Bradic
In this paper, we propose a new random forest algorithm that constructs the trees using a novel adaptive split-balancing method. Rather than relying on the widely-used random featu…
Semi-supervised linear regression: enhancing efficiency and robustness in high dimensions
Kai Chen, Yuqian Zhang
In semi-supervised learning, the prevailing understanding suggests that observing additional unlabeled samples improves estimation accuracy for linear parameters only in the case o…
High-dimensional semi-supervised learning: in search for optimal inference of the mean
Yuqian Zhang, Jelena Bradic
A fundamental challenge in semi-supervised learning lies in the observed data's disproportional size when compared with the size of the data collected with missing outcomes. An imp…